Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable
frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and
preferences in a suggestion provided using just place-based services. Current generation of place-based
services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of
using social recommender systems was discussed which contains capability of identifying user’s interests
and preferences and based on them and user’s current place, it offers some suggestions. Social
recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible
to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can
acquire their favorite information and services.
Point of-interest recommendation for location promotion in location-based soc...CloudTechnologies
Point-of-interest Recommendation for Location Promotion in Location-based Social Networks Data Mining IEEE-2017 Java Project Abstract From Cloud Technologies Hyderabad
Designing a recommender system based on social networks and location based se...IJMIT JOURNAL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and preferences in a suggestion provided using just place-based services. Current generation of place-based services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of using social recommender systems was discussed which contains capability of identifying user’s interests and preferences and based on them and user’s current place, it offers some suggestions. Social recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can acquire their favorite information and services.
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORKcsandit
The circulation of the social networks and the evolution of the mobile phone devices has led to a
big usage of location based social networks application such as Foursquare, Twitter, Swarm
and Zomato on mobile phone devices mean that huge dataset which is containing a blend of
information about users behaviour’s, social society network of each users and also information
about each of venues, all these information available in mobile location recommendation
system .These datasets are much more different from those which is used in online recommender
systems, these datasets have more information and details about the users and the venues which
is allowing to have more clear result with much more higher accuracy of the analysing in the
result.
In this paper we examine the users behaviour’s and the popularity of the venue through a large
check-ins dataset from a location based social services, Foursquare: by using large scale
dataset containing both user check-in and location information .Our analysis expose across 3
different cities.On analysis of these dataset reveal a different mobility habits, preferring places
and also location patterns in the user personality. This information about the users behaviour’s
and each of the location popularity can be used to know the recommendation systems and to
predict the next move of the users depending on the categories that the users attend to visit and
according to the history of each users check-ins.
A location based movie recommender systemijfcstjournal
Available recommender systems mostly provide recommendations based on the users’ preferences by
utilizing traditional methods such as collaborative filtering which only relies on the similarities between users and items. However, collaborative filtering might lead to provide poor recommendation because it does not rely on other useful available data such as users’ locations and hence the accuracy of the recommendations could be very low and inefficient. This could be very obvious in the systems that locations would affect users’ preferences highly such as movie recommender systems. In this paper a new locationbased movie recommender system based on the collaborative filtering is introduced for enhancing the
accuracy and the quality of recommendations. In this approach, users’ locations have been utilized and
take in consideration in the entire processing of the recommendations and peer selections. The potential of
the proposed approach in providing novel and better quality recommendations have been discussed through experiments in real datasets.
Finding nearest Neighbor in Geo-Social Query Processingrahulmonikasharma
Recording the region of people using location-acquisition technologies, such as GPS, allows generating life patterns, which associate people to places they habitually visit. Considering life patterns as edges that connect users of a social network to geographical entities on a spatial network, improves the social network, providing an integrated geo-social graph. Queries over such graph excerpt information on users, with respect to their location history, and excerpt information on geographical entities in correspondence with users who normally visit these entities. A repeated type of query in spatial networks (e.g., road networks) is to find the k- nearest neighbors (k-NN) of a given query objects. With these networks, the distances between objects depend on their network connectivity and it is expensive to compute the distances (e.g., shortest paths) between objects. We present the concept of a geo-social graph that is based on life patterns, where users are connected to geographical entities using life-pattern edges more specifically to allow finding a group of users in a Geo-Social network whose members are close to each other both socially and geographically. We proposed a new approach to find the group of k users who are geo-socially attached to each other and satisfy the all the query points. We used the Bottom up pruning technique for effective pruning of geo-social group queries. An important contribution of this work is in illustrating the usefulness and the feasibility of maintaining and querying integrated geo-social graphs.
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
Point of-interest recommendation for location promotion in location-based soc...CloudTechnologies
Point-of-interest Recommendation for Location Promotion in Location-based Social Networks Data Mining IEEE-2017 Java Project Abstract From Cloud Technologies Hyderabad
Designing a recommender system based on social networks and location based se...IJMIT JOURNAL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and preferences in a suggestion provided using just place-based services. Current generation of place-based services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of using social recommender systems was discussed which contains capability of identifying user’s interests and preferences and based on them and user’s current place, it offers some suggestions. Social recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can acquire their favorite information and services.
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORKcsandit
The circulation of the social networks and the evolution of the mobile phone devices has led to a
big usage of location based social networks application such as Foursquare, Twitter, Swarm
and Zomato on mobile phone devices mean that huge dataset which is containing a blend of
information about users behaviour’s, social society network of each users and also information
about each of venues, all these information available in mobile location recommendation
system .These datasets are much more different from those which is used in online recommender
systems, these datasets have more information and details about the users and the venues which
is allowing to have more clear result with much more higher accuracy of the analysing in the
result.
In this paper we examine the users behaviour’s and the popularity of the venue through a large
check-ins dataset from a location based social services, Foursquare: by using large scale
dataset containing both user check-in and location information .Our analysis expose across 3
different cities.On analysis of these dataset reveal a different mobility habits, preferring places
and also location patterns in the user personality. This information about the users behaviour’s
and each of the location popularity can be used to know the recommendation systems and to
predict the next move of the users depending on the categories that the users attend to visit and
according to the history of each users check-ins.
A location based movie recommender systemijfcstjournal
Available recommender systems mostly provide recommendations based on the users’ preferences by
utilizing traditional methods such as collaborative filtering which only relies on the similarities between users and items. However, collaborative filtering might lead to provide poor recommendation because it does not rely on other useful available data such as users’ locations and hence the accuracy of the recommendations could be very low and inefficient. This could be very obvious in the systems that locations would affect users’ preferences highly such as movie recommender systems. In this paper a new locationbased movie recommender system based on the collaborative filtering is introduced for enhancing the
accuracy and the quality of recommendations. In this approach, users’ locations have been utilized and
take in consideration in the entire processing of the recommendations and peer selections. The potential of
the proposed approach in providing novel and better quality recommendations have been discussed through experiments in real datasets.
Finding nearest Neighbor in Geo-Social Query Processingrahulmonikasharma
Recording the region of people using location-acquisition technologies, such as GPS, allows generating life patterns, which associate people to places they habitually visit. Considering life patterns as edges that connect users of a social network to geographical entities on a spatial network, improves the social network, providing an integrated geo-social graph. Queries over such graph excerpt information on users, with respect to their location history, and excerpt information on geographical entities in correspondence with users who normally visit these entities. A repeated type of query in spatial networks (e.g., road networks) is to find the k- nearest neighbors (k-NN) of a given query objects. With these networks, the distances between objects depend on their network connectivity and it is expensive to compute the distances (e.g., shortest paths) between objects. We present the concept of a geo-social graph that is based on life patterns, where users are connected to geographical entities using life-pattern edges more specifically to allow finding a group of users in a Geo-Social network whose members are close to each other both socially and geographically. We proposed a new approach to find the group of k users who are geo-socially attached to each other and satisfy the all the query points. We used the Bottom up pruning technique for effective pruning of geo-social group queries. An important contribution of this work is in illustrating the usefulness and the feasibility of maintaining and querying integrated geo-social graphs.
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
Combining Behaviors and Demographics to Segment Online Audiences:Experiments ...Joni Salminen
Link to article: https://www.springerprofessional.de/en/combining-behaviors-and-demographics-to-segment-online-audiences/16204306
CITE: Jansen, Bernard J., Jung, S., Salminen, J., An, J. and Kwak, H. (2018), “Combining Behaviors and Demographics to Segment Online Audiences: Experiments with a YouTube Channel”, Proceedings of the 5th International Conference of Internet Science (INSCI 2018), Springer, St. Petersburg, Russia.
Link to Automatic Persona Generation: https://persona.qcri.org
User Studies for APG: How to support system development with user feedback?Joni Salminen
Presentation at QCRI's Science Monday of the Social Computing group. January 14, 2019. Doha, Qatar. Access the Automatic Persona Generation system: https://persona.qcri.org
Research Roadmap for Automatic Persona Generation (2018)Joni Salminen
Automatic Persona Generation (APG) is a system and methodology developed at Qatar Computing Research Institute, Hamad Bin Khalifa University. Read more: https://persona.qcri.org
The goal of Automatic Persona Generation is to give faces to social and online analytics data. Personas can be generated from YouTube, Facebook, and Google Analytics data.
If you are interested in research collaboration, please contact Professor Jim Jansen at bjansen@hbku.edu.qa
In the online world, user engagement refers to the quality of the user experience that emphasizes the phenomena associated with wanting to use an application longer and frequently. This talk looks at the role of Big Data in measuring user engagement. It does so through two case studies on using absence time, within sessions and across sessions.
Presentation at "Data-Driven Business Day" at Strata + HW Barcelona 2014.
Friend Recommendation on Social Network Site Based on Their Life Stylepaperpublications3
Abstract: Social network sites attracted millions of users. In the social network sites, a user can register other users as friends and enjoy communication. Existing social networking sites recommend friends to users based on their social graphs, which may not be appropriate. In proposed system friends recommends to users based on their life styles instead of social graphs. It done by means of sensor rich smart- phone serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. Unsupervised learning method is used. Achieve an efficient activity Recognition and reduce the false positive of Friend Recommendation. Friendbook integrates a feedback mechanism. Finally the results show that the recommendations accurately reflect the preferences of users in choosing friends.
Maede Kiani Sarkaleh, Mehregan Mahdavi and Mahsa BaniardalanIJMIT JOURNAL
Today, mobile devices are widely used by many tourists. They can use mobile for accessing information
about all sightseeing around the world. On the other hand, it seems essential to personalize the content due
to diversity of learners and variation of the tools they use. On the whole, the goal for personalization is to
suggest a collection of comprehensive activities, taking into consideration factors such as location, user
preferences and interests and so on. One of the applications of recommender systems is in tourism industry.
Web search engines help users find useful information on the WWW. However, when the same
query is submitted by different users, typical search engines return the same result regardless of who
submitted the query. Generally, each user has different information needs for his/her query. Therefore,
the search results should be adapted to users with different information needs. So, there is need of
several approaches to adapting search results according to each user’s need for relevant information
without any user effort. Such search systems that adapt to each user’s preferences can be achieved by
constructing user profiles based on modified collaborative filtering with detailed analysis of user’s
browsing history.
There are three possible types of web search system which can provide personalized
information: (1) systems using relevance feedback, (2) systems in which users register their interest, and
(3) systems that recommend information based on user’s history. In first technique, users have to provide
feedback on relevant or irrelevant judgments which is time consuming and the second one needs
registration of users with their static interests which need extra effort from user. So, the third technique
is best in which users don’t have to give explicit rating; relevancy automatically tracked by user
behavior with search results and history of data usage. It doesn’t require registration of interests; it
captures changing interests of user dynamically by itself. The result section shows that user’s browsing
history allows each user to perform more fine-grained search by capturing changes of each user’s
preferences without any user effort. Users need less time to find the relevant snippet in personalized
search results compared to original results
Web search engines help users find useful information on the WWW. However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search results should be adapted to users with different information needs. So, there is need of
several approaches to adapting search results according to each user’s need for relevant information without any user effort. Such search systems that adapt to each user’s preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user’s browsing history. There are three possible types of web search system which can provide personalized information: (1) systems using relevance feedback, (2) systems in which users register their interest, and (3) systems that recommend information based on user’s history. In first technique, users have to provide feedback on relevant or irrelevant judgments which is time consuming and the second one needs
registration of users with their static interests which need extra effort from user. So, the third technique is best in which users don’t have to give explicit rating; relevancy automatically tracked by user behavior with search results and history of data usage. It doesn’t require registration of interests; it captures changing interests of user dynamically by itself. The result section shows that user’s browsing history allows each user to perform more fine-grained search by capturing changes of each user’s
preferences without any user effort. Users need less time to find the relevant snippet in personalized
search results compared to original results.
Implementation of Privacy Policy Specification System for User Uploaded Image...rahulmonikasharma
The regular use of social networking websites and application encompasses the collection and retention of personal and very often sensitive information about users. This information needs to remain private and each social network owns a privacy policy that describes in-depth how user’s information is managed and published. As there is increasing use of images for sharing through social sites, maintaining privacy has become a major problem. In light of these incidents, the need of tools to aid users control access to their shared content is necessary. This problem can be proposed by using an Privacy Policy Specification system to help users compose privacy settings for their shared images. Toward addressing this need, we propose Privacy Policy Specification system to help users to specify privacy settings for their images. Privacy Policy Specification System configure a policy for a group and apply appropriate policies (comment, share, expiry, download) on image for sharing in the group.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
Happy Hours is a time-limited offer strategy which attracts people by providing maximum
discounts. People are not aware about these offers going in their nearby areas because of their busy
schedule. Mobile device is ubiquity available with customers everywhere and all the time which can help
in personalizing advertisements with the help of spatial information and various other parameters.
Existing systems use SMS to notify users about advertisements and SMS has limitations. Traditional
systems use request-response pattern where user needs to always submit query which ultimately
degrades user experience. Also mobile visualization ends up with too much too visualize in a too little
display area. Our proposed system considers all of the above issues and leverages the strengths of
proactive model and context-aware personalized dashboard for Happy Hours and their Deals
recommendations.
A LOCATION-BASED MOVIE RECOMMENDER SYSTEM USING COLLABORATIVE FILTERINGijfcstjournal
Available recommender systems mostly provide recommendations based on the users’ preferences by
utilizing traditional methods such as collaborative filtering which only relies on the similarities between
users and items. However, collaborative filtering might lead to provide poor recommendation because it
does not rely on other useful available data such as users’ locations and hence the accuracy of the
recommendations could be very low and inefficient. This could be very obvious in the systems that locations
would affect users’ preferences highly such as movie recommender systems. In this paper a new locationbased movie recommender system based on the collaborative filtering is introduced for enhancing the
accuracy and the quality of recommendations. In this approach, users’ locations have been utilized and
take in consideration in the entire processing of the recommendations and peer selections. The potential of
the proposed approach in providing novel and better quality recommendations have been discussed
through experiments in real datasets.
Determining Strategic Value of Online Social Engagementsinventionjournals
Over the past few decades social networking connections through individuals and open publishing in general have rapidly became a popular tool for maintaining relationships, communicating and expanding businesses. Individuals invest hours in building social capital and their social identify (SID) via online engagements. We present a methodology to quantify the multitude of artifacts that can be derived from online social engagements and develop a framework that measures the value of an individual's online social engagements. ASID value is used to deliver a score for each individual user; a score that will assist you in understanding your return on investment (ROI)and social capital from your online social networking activities. The framework creates a score to support and determine which specific engagements add and increase your personal value chain. This score can provide benefit to users for career, personal, and business opportunities.
Integration of Bayesian Theory and Association Rule Mining in Predicting User...Editor IJCATR
Bayesian theory and association rule mining methods are artificial intelligence techniques that have been used in various computing fields, especially in machine learning. Internet has been considered as an easy ground for vices like radicalization because of its diverse nature and ease of information access. These vices could be managed using recommender systems methods which are used to deliver users’ preference data based on their previous interests and in relation with the community around the user. The recommender systems are divided into two broad categories, i.e. collaborative systems which considers users which share the same preferences as the user in question and content-based recommender systems tends to recommend websites similar to those already liked by the user. Recent research and information from security organs indicate that, online radicalization has been growing at an alarming rate. The paper reviews in depth what has been carried out in recommender systems and looks at how these methods could be combined to from a strong system to monitor and manage online menace as a result of radicalization. The relationship between different websites and the trend from continuous access of these websites forms the basis for probabilistic reasoning in understanding the users’ behavior. Association rule mining method has been widely used in recommender systems in profiling and generating users’ preferences. To add probabilistic reasoning considering internet magnitude and more so in social media, Bayesian theory is incorporated. Combination of this two techniques provides better analysis of the results thereby adding reliability and knowledge to the results.
Integration of Bayesian Theory and Association Rule Mining in Predicting User...Editor IJCATR
Bayesian theory and association rule mining methods are artificial intelligence techniques that have been used in various
computing fields, especially in machine learning. Internet has been considered as an easy ground for vices like radicalization because
of its diverse nature and ease of information access. These vices could be managed using recommender systems methods which are
used to deliver users’ preference data based on their previous interests and in relation with the community around the user. The
recommender systems are divided into two broad categories, i.e. collaborative systems which considers users which share the same
preferences as the user in question and content-based recommender systems tends to recommend websites similar to those already
liked by the user. Recent research and information from security organs indicate that, online radicalization has been growing at an
alarming rate. The paper reviews in depth what has been carried out in recommender systems and looks at how these methods could be
combined to from a strong system to monitor and manage online menace as a result of radicalization. The relationship between
different websites and the trend from continuous access of these websites forms the basis for probabilistic reasoning in understanding
the users’ behavior. Association rule mining method has been widely used in recommender systems in profiling and generating users’
preferences. To add probabilistic reasoning considering internet magnitude and more so in social media, Bayesian theory is
incorporated. Combination of this two techniques provides better analysis of the results thereby adding reliability and knowledge to the
results.
Combining Behaviors and Demographics to Segment Online Audiences:Experiments ...Joni Salminen
Link to article: https://www.springerprofessional.de/en/combining-behaviors-and-demographics-to-segment-online-audiences/16204306
CITE: Jansen, Bernard J., Jung, S., Salminen, J., An, J. and Kwak, H. (2018), “Combining Behaviors and Demographics to Segment Online Audiences: Experiments with a YouTube Channel”, Proceedings of the 5th International Conference of Internet Science (INSCI 2018), Springer, St. Petersburg, Russia.
Link to Automatic Persona Generation: https://persona.qcri.org
User Studies for APG: How to support system development with user feedback?Joni Salminen
Presentation at QCRI's Science Monday of the Social Computing group. January 14, 2019. Doha, Qatar. Access the Automatic Persona Generation system: https://persona.qcri.org
Research Roadmap for Automatic Persona Generation (2018)Joni Salminen
Automatic Persona Generation (APG) is a system and methodology developed at Qatar Computing Research Institute, Hamad Bin Khalifa University. Read more: https://persona.qcri.org
The goal of Automatic Persona Generation is to give faces to social and online analytics data. Personas can be generated from YouTube, Facebook, and Google Analytics data.
If you are interested in research collaboration, please contact Professor Jim Jansen at bjansen@hbku.edu.qa
In the online world, user engagement refers to the quality of the user experience that emphasizes the phenomena associated with wanting to use an application longer and frequently. This talk looks at the role of Big Data in measuring user engagement. It does so through two case studies on using absence time, within sessions and across sessions.
Presentation at "Data-Driven Business Day" at Strata + HW Barcelona 2014.
Friend Recommendation on Social Network Site Based on Their Life Stylepaperpublications3
Abstract: Social network sites attracted millions of users. In the social network sites, a user can register other users as friends and enjoy communication. Existing social networking sites recommend friends to users based on their social graphs, which may not be appropriate. In proposed system friends recommends to users based on their life styles instead of social graphs. It done by means of sensor rich smart- phone serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. Unsupervised learning method is used. Achieve an efficient activity Recognition and reduce the false positive of Friend Recommendation. Friendbook integrates a feedback mechanism. Finally the results show that the recommendations accurately reflect the preferences of users in choosing friends.
Maede Kiani Sarkaleh, Mehregan Mahdavi and Mahsa BaniardalanIJMIT JOURNAL
Today, mobile devices are widely used by many tourists. They can use mobile for accessing information
about all sightseeing around the world. On the other hand, it seems essential to personalize the content due
to diversity of learners and variation of the tools they use. On the whole, the goal for personalization is to
suggest a collection of comprehensive activities, taking into consideration factors such as location, user
preferences and interests and so on. One of the applications of recommender systems is in tourism industry.
Web search engines help users find useful information on the WWW. However, when the same
query is submitted by different users, typical search engines return the same result regardless of who
submitted the query. Generally, each user has different information needs for his/her query. Therefore,
the search results should be adapted to users with different information needs. So, there is need of
several approaches to adapting search results according to each user’s need for relevant information
without any user effort. Such search systems that adapt to each user’s preferences can be achieved by
constructing user profiles based on modified collaborative filtering with detailed analysis of user’s
browsing history.
There are three possible types of web search system which can provide personalized
information: (1) systems using relevance feedback, (2) systems in which users register their interest, and
(3) systems that recommend information based on user’s history. In first technique, users have to provide
feedback on relevant or irrelevant judgments which is time consuming and the second one needs
registration of users with their static interests which need extra effort from user. So, the third technique
is best in which users don’t have to give explicit rating; relevancy automatically tracked by user
behavior with search results and history of data usage. It doesn’t require registration of interests; it
captures changing interests of user dynamically by itself. The result section shows that user’s browsing
history allows each user to perform more fine-grained search by capturing changes of each user’s
preferences without any user effort. Users need less time to find the relevant snippet in personalized
search results compared to original results
Web search engines help users find useful information on the WWW. However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search results should be adapted to users with different information needs. So, there is need of
several approaches to adapting search results according to each user’s need for relevant information without any user effort. Such search systems that adapt to each user’s preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user’s browsing history. There are three possible types of web search system which can provide personalized information: (1) systems using relevance feedback, (2) systems in which users register their interest, and (3) systems that recommend information based on user’s history. In first technique, users have to provide feedback on relevant or irrelevant judgments which is time consuming and the second one needs
registration of users with their static interests which need extra effort from user. So, the third technique is best in which users don’t have to give explicit rating; relevancy automatically tracked by user behavior with search results and history of data usage. It doesn’t require registration of interests; it captures changing interests of user dynamically by itself. The result section shows that user’s browsing history allows each user to perform more fine-grained search by capturing changes of each user’s
preferences without any user effort. Users need less time to find the relevant snippet in personalized
search results compared to original results.
Implementation of Privacy Policy Specification System for User Uploaded Image...rahulmonikasharma
The regular use of social networking websites and application encompasses the collection and retention of personal and very often sensitive information about users. This information needs to remain private and each social network owns a privacy policy that describes in-depth how user’s information is managed and published. As there is increasing use of images for sharing through social sites, maintaining privacy has become a major problem. In light of these incidents, the need of tools to aid users control access to their shared content is necessary. This problem can be proposed by using an Privacy Policy Specification system to help users compose privacy settings for their shared images. Toward addressing this need, we propose Privacy Policy Specification system to help users to specify privacy settings for their images. Privacy Policy Specification System configure a policy for a group and apply appropriate policies (comment, share, expiry, download) on image for sharing in the group.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
Happy Hours is a time-limited offer strategy which attracts people by providing maximum
discounts. People are not aware about these offers going in their nearby areas because of their busy
schedule. Mobile device is ubiquity available with customers everywhere and all the time which can help
in personalizing advertisements with the help of spatial information and various other parameters.
Existing systems use SMS to notify users about advertisements and SMS has limitations. Traditional
systems use request-response pattern where user needs to always submit query which ultimately
degrades user experience. Also mobile visualization ends up with too much too visualize in a too little
display area. Our proposed system considers all of the above issues and leverages the strengths of
proactive model and context-aware personalized dashboard for Happy Hours and their Deals
recommendations.
A LOCATION-BASED MOVIE RECOMMENDER SYSTEM USING COLLABORATIVE FILTERINGijfcstjournal
Available recommender systems mostly provide recommendations based on the users’ preferences by
utilizing traditional methods such as collaborative filtering which only relies on the similarities between
users and items. However, collaborative filtering might lead to provide poor recommendation because it
does not rely on other useful available data such as users’ locations and hence the accuracy of the
recommendations could be very low and inefficient. This could be very obvious in the systems that locations
would affect users’ preferences highly such as movie recommender systems. In this paper a new locationbased movie recommender system based on the collaborative filtering is introduced for enhancing the
accuracy and the quality of recommendations. In this approach, users’ locations have been utilized and
take in consideration in the entire processing of the recommendations and peer selections. The potential of
the proposed approach in providing novel and better quality recommendations have been discussed
through experiments in real datasets.
Determining Strategic Value of Online Social Engagementsinventionjournals
Over the past few decades social networking connections through individuals and open publishing in general have rapidly became a popular tool for maintaining relationships, communicating and expanding businesses. Individuals invest hours in building social capital and their social identify (SID) via online engagements. We present a methodology to quantify the multitude of artifacts that can be derived from online social engagements and develop a framework that measures the value of an individual's online social engagements. ASID value is used to deliver a score for each individual user; a score that will assist you in understanding your return on investment (ROI)and social capital from your online social networking activities. The framework creates a score to support and determine which specific engagements add and increase your personal value chain. This score can provide benefit to users for career, personal, and business opportunities.
Integration of Bayesian Theory and Association Rule Mining in Predicting User...Editor IJCATR
Bayesian theory and association rule mining methods are artificial intelligence techniques that have been used in various computing fields, especially in machine learning. Internet has been considered as an easy ground for vices like radicalization because of its diverse nature and ease of information access. These vices could be managed using recommender systems methods which are used to deliver users’ preference data based on their previous interests and in relation with the community around the user. The recommender systems are divided into two broad categories, i.e. collaborative systems which considers users which share the same preferences as the user in question and content-based recommender systems tends to recommend websites similar to those already liked by the user. Recent research and information from security organs indicate that, online radicalization has been growing at an alarming rate. The paper reviews in depth what has been carried out in recommender systems and looks at how these methods could be combined to from a strong system to monitor and manage online menace as a result of radicalization. The relationship between different websites and the trend from continuous access of these websites forms the basis for probabilistic reasoning in understanding the users’ behavior. Association rule mining method has been widely used in recommender systems in profiling and generating users’ preferences. To add probabilistic reasoning considering internet magnitude and more so in social media, Bayesian theory is incorporated. Combination of this two techniques provides better analysis of the results thereby adding reliability and knowledge to the results.
Integration of Bayesian Theory and Association Rule Mining in Predicting User...Editor IJCATR
Bayesian theory and association rule mining methods are artificial intelligence techniques that have been used in various
computing fields, especially in machine learning. Internet has been considered as an easy ground for vices like radicalization because
of its diverse nature and ease of information access. These vices could be managed using recommender systems methods which are
used to deliver users’ preference data based on their previous interests and in relation with the community around the user. The
recommender systems are divided into two broad categories, i.e. collaborative systems which considers users which share the same
preferences as the user in question and content-based recommender systems tends to recommend websites similar to those already
liked by the user. Recent research and information from security organs indicate that, online radicalization has been growing at an
alarming rate. The paper reviews in depth what has been carried out in recommender systems and looks at how these methods could be
combined to from a strong system to monitor and manage online menace as a result of radicalization. The relationship between
different websites and the trend from continuous access of these websites forms the basis for probabilistic reasoning in understanding
the users’ behavior. Association rule mining method has been widely used in recommender systems in profiling and generating users’
preferences. To add probabilistic reasoning considering internet magnitude and more so in social media, Bayesian theory is
incorporated. Combination of this two techniques provides better analysis of the results thereby adding reliability and knowledge to the
results.
The Verification Of Virtual Community Member’s Socio-Demographic Profileacijjournal
This article considers the current problem of investigation and development of method of web-members’
socio-demographic characteristics’ profile validation based on analysis of socio-demographic
characteristics. The topicality of the paper is determined by the necessity to identify the web-community
member by means of computer-linguistic analysis of their information track (all information about webcommunity
members, which posted on the Internet). The formal model of basic socio-demographic
characteristics of virtual communities’ member is formed. The algorithm of these characteristics
verification is developed.
THE VERIFICATION OF VIRTUAL COMMUNITY MEMBER’S SOCIO-DEMOGRAPHIC PROFILE acijjournal
This article considers the current problem of investigation and development of method of web-members’
socio-demographic characteristics’ profile validation based on analysis of socio-demographic
characteristics. The topicality of the paper is determined by the necessity to identify the web-community
member by means of computer-linguistic analysis of their information track (all information about webcommunity
members, which posted on the Internet). The formal model of basic socio-demographic
characteristics of virtual communities’ member is formed. The algorithm of these characteristics
verification is developed.
The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Towards a hybrid recommendation approach using a community detection and eval...IJECEIAES
In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similarities, recommendation algorithms generate more accurate suggestions. In this article, we propose a novel approach that combines community detection and recommendation algorithms into a single framework to provide learners with personalized recommendations and opportunities for collaborative learning. Our proposed approach consists of three steps: first, applying the maximal clique-based algorithm to detect learning communities with common characteristics and interests; second, evaluating learners within their communities using static and dynamic evaluation; and third, generating personalized recommendations within each detected cluster using a recommendation system based on correlation and co-occurrence. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real-world dataset. Our results show that our approach outperforms existing methods in terms of modularity, precision, and accuracy.
Personalized E-commerce based recommendation systems using deep-learning tech...IAESIJAI
As technology is surpassing each day, with the variation of personalized drifts
relevant to the explicit behavior of users using the internet. Recommendation
systems use predictive mechanisms like predicting a rating that a customer
could give on a specific item. This establishes a ranked list of items according
to the preferences each user makes concerning exhibiting personalized
recommendations. The existing recommendation techniques are efficient in
systematically creating recommendation techniques. This approach
encounters many challenges such as determining the accuracy, scalability, and
data sparsity. Recently deep learning attains significant research to enhance
the performance to improvise feature specification in learning the efficiency
of retrieving the necessary information as well as a recommendation system
approach. Here, we provide a thorough review of the deep-learning
mechanism focused on the learning-rates-based prediction approach modeled
to articulate the widespread summary for the state-of-art techniques. The
novel techniques ensure the incorporation of innovative perspectives to
pertain to the unique and exciting growth in this field.
Identifying the Factors Affecting Users’ Adoption of Social NetworkingWaqas Tariq
Through the rapid expansion of information and communication technologies, social networking sites have received much more attention in the scope of internet communication. Success of a social web primarily depends on users’ satisfaction. In this context, this study aims to identify the influencing factors that affect users’ satisfaction towards social networking site use. A multidimensional model has been proposed based on the Information Quality, System Quality, Environmental and Affective dimensions to assess the effects of key variables – Semantic Intention, Usability, Web-Page Aesthetics, Subjective Norm and Trust- on users’ satisfaction. Facebook was chosen as a focused social networking site, because of its popularity. A comprehensive survey instrument was applied to 203 Facebook users. Also, Structural Equation Modeling, particularly Partial Least Square, was conducted to analyze the proposed research model. As a result, proposed multidimensional research model predicts the factors influencing users’ satisfaction towards social networking site use and relationships among these factors. The findings of this research will be valuable for literature by analyzing the influencing factors that have not been previously researched in the context of social networking satisfaction area.
Similar to DESIGNING A RECOMMENDER SYSTEM BASED ON SOCIAL NETWORKS AND LOCATION BASED SERVICES (20)
MULTIMODAL COURSE DESIGN AND IMPLEMENTATION USING LEML AND LMS FOR INSTRUCTIO...IJMIT JOURNAL
Traditionally, teaching has been centered around classroom delivery. However, the onslaught of the
COVID-19 pandemic has cultivated usage of technology, teaching, and learning methodologies for course
delivery. We investigate and describe different modes of course delivery that maintain the integrity of
teaching and learning. This paper answers to the research questions: 1) What course delivery method our
academic institutions use and why? 2) How can instructors validate the guidelines of the institutions? 3)
How courses should be taught to provide student learning outcomes? Using the Learning Environment
Modeling Language (LEML), we investigate the design and implementation of courses for delivery in the
following environments: face-to-face, online synchronous, asynchronous, hybrid, and hyflex. A good
course design and implementation are key components of instructional alignment. Furthermore, we
demonstrate how to design, implement, and deliver courses in synchronous, asynchronous, and hybrid
modes and describe our proposed enhancements to LEML.
Novel R&D Capabilities as a Response to ESG Risks-Lessons From Amazon’s Fusio...IJMIT JOURNAL
Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
NOVEL R & D CAPABILITIES AS A RESPONSE TO ESG RISKS- LESSONS FROM AMAZON’S FU...IJMIT JOURNAL
Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
Building on this premise, this paper conducts an empirical analysis, utilizing reliable firms data on ESG
risk and brand value, with a focus on 100 global R&D leader firms. It analyzes R&D and actions for ESG
risk mitigation, and assesses the development of new functions that fulfill F + ESG optimization through
R&D. The analysis also highlights the significance of network externality effects, with a specific focus on
Amazon, a leading R&D company, providing insights into the direction for transforming R&D strategies
towards F + ESG optimization.
The dynamics of stakeholder engagement in F + ESG optimization are indicated with the example of
amazon's activities. Through the analysis, it became evident that Amazon's capacity encompassing growth
and scalability, specifically its ability to grow and expand, is accelerating high-level research and
development by gaining the trust of stakeholders in the "synergy through R&D-driven ESG risk
mitigation."
Finally, as examples of these initiatives, the paper discussed the Climate Pledge led by Amazon and the
transformation of Japan's management system.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
These days the security provided by the computer systems is a big issue as it always has the threats of
cyber-attacks like IP address spoofing, Denial of Service (DOS), token impersonation, etc. The security
provided by the blue team operations tends to be costly if done in large firms as a large number of systems
need to be protected against these attacks. This leads these firms to turn to less costly security
configurations like IDS Suricata and IDS Snort. The main theme of the project is to improve the services
provided by Snort which is a tool used in creating a vague defense against cyber-attacks like DDOS
attacks which are done on both physical and network layers. These attacks in turn result in loss of
extremely important data. The rules defined in this project will result in monitoring traffic, analyzing it,
and taking appropriate action to not only stop the attack but also locate its source IP address. This whole
process uses different tools other than Snort like Wireshark, Wazuh and Splunk. The product of this will
result in not only the detection of the attack but also the source IP address of the machine on which the
attack is initiated and completed. The end product of this research will result in sets of default rules for the
Snort tool which will not only be able to provide better security than its previous versions but also be able
to provide the user with the IP address of the attacker or the person conducting the attack. The system
involves the integration of Wazuh with Snort tool in order to make it more efficient than IDS Suricata
which is another intrusion detection system capable of detecting all these types of attacks as mentioned.
Splunk is another tool used in this project which increases the firewall efficiency to pass the no. of bits to
be scanned and the no. of bits scanned successfully. Wazuh is used in this system as it is the best choice for
traffic monitoring and incident response than any other of its alternatives in the market. Since this system
is used in firms which are known to handle big amounts of data and for this purpose, we use Splunk tool as
it is very efficient in handling big amounts of data. Wireshark is used in this system in order to give the IDS
automation in its capability to capture and report the malicious packets found during the network scan. All
of this gives the IDS a capability of a low budget automated threat detection system. This paper gives
complete guidelines for authors submitting papers for the AIRCC Journals.
Artificial Intelligence (AI) has rapidly become a critical technology for businesses seeking to improve
efficiency and profitability. One area where AI is proving particularly impactful is in service operations
management, where it is used to create AI-powered service operations (AIServiceOps) that deliver highvalue services to customers. AIServiceOps involve the use of AI to automate and optimize various business
processes, such as customer service, sales, marketing, and supply chain management. The rapid
development of Artificial Intelligence has prompted many changes in the field of Information Technology
(IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered
new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on
the Business Value Impact of Artificial intelligence (AI) Powered IT Service Operations. It can help IT
build optimized business resilience by creating value in complex and ever-changing environments as
product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps
creates business value and sustainability, basically how AIServiceOps makes the IT staff liberation from a
low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the
research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper
provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation
tool. The paper presents a case study of a company that implemented AI-powered service operations
(AIServiceOps) and analyzes the resulting business outcomes. The study shows that AIServiceOps can
significantly improve service delivery, reduce response times, and increase customer satisfaction.
Furthermore, it demonstrates how AIServiceOps can deliver substantial cost savings, such as reducing
labor costs and minimizing downtime.
MEDIATING AND MODERATING FACTORS AFFECTING READINESS TO IOT APPLICATIONS: THE...IJMIT JOURNAL
Although IOT seems to be the upcoming trend, it is still in its infancy; especially in the banking industry.
There is a clear gap in literature, as only few studies identify factors affecting readiness to IOT
applications in banks in general, and almost negligible investigations on mediating and moderating
factors. Accordingly, this research aims to investigate the main factors that affect employees’ readiness to
IOT applications, while highlighting the mediating and moderating factors in the Egyptian banking sector.
The importance of Egypt stems from its high population and steady steps taken towards technology
adoption. 479 valid questionnaires were distributed over HR employees in banks. Data collected was
statistically analysed using Regression and SEM. Results showed a significant impact of ‘Security’,
‘Networking’, ‘Software Development’ and ‘Regulations’ on ‘readiness to IOT applications. Thus, the
readiness acceptance level is high‘Security’ and ‘User Intention’ were proven to mediate the relationship
between research variables and readiness to IOT applications, and only a partial moderation role was
proven for ‘Efficiency’. The study contributes to increasing literature on IOT applications in general, and
fills a gap on the Egyptian banking context in particular. Finally, it provides decision makers at banks with
useful guidelines on how to optimally promote IOT applications among employees.
EFFECTIVELY CONNECT ACQUIRED TECHNOLOGY TO INNOVATION OVER A LONG PERIODIJMIT JOURNAL
IT (Information and Communication Technology) companies are facing the dilemma of decreasing
productivity despite increasing research and development efforts. M&A (Merger and Acquisition) is being
considered as a breakthrough solution. From existing research, it has been pointed out that M&A leads to
the emergence of new innovations. Purpose of this study was to discuss the efficient ways of acquisition and
to resolve the dilemma of productivity decline by clarifying how the technology obtained through M&A
leads to the creation of new innovations. Hypothesis 1 was that the technology acquired through M&A is
utilized for innovation creation, Hypothesis 2 was that the acquired technology is utilized over a long
period of time, and Hypothesis 3 was that a long-term utilization has a positive impact on corporate
performance. The results, using sports prosthetics as a case study and using patents as a proxy variable,
confirmed all the hypotheses set. We have revealed that long-term utilization of technology obtained
through M&A is effective for creating new innovations.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of information technology and management
4th International Conference on Cloud, Big Data and IoT (CBIoT 2023)IJMIT JOURNAL
4th International Conference on Cloud, Big Data and IoT (CBIoT 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and IoT. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and IoT.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Cloud, Big Data and IoT.
TRANSFORMING SERVICE OPERATIONS WITH AI: A CASE FOR BUSINESS VALUEIJMIT JOURNAL
Artificial Intelligence (AI) has rapidly become a critical technology for businesses seeking to improve
efficiency and profitability. One area where AI is proving particularly impactful is in service operations
management, where it is used to create AI-powered service operations (AIServiceOps) that deliver highvalue services to customers. AIServiceOps involve the use of AI to automate and optimize various business
processes, such as customer service, sales, marketing, and supply chain management. The rapid
development of Artificial Intelligence has prompted many changes in the field of Information Technology
(IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered
new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on
the Business Value Impact of Artificial intelligence (AI) Powered IT Service Operations. It can help IT
build optimized business resilience by creating value in complex and ever-changing environments as
product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps
creates business value and sustainability, basically how AIServiceOps makes the IT staff liberation from a
low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the
research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper
provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation
tool. The paper presents a case study of a company that implemented AI-powered service operations
(AIServiceOps) and analyzes the resulting business outcomes. The study shows that AIServiceOps can
significantly improve service delivery, reduce response times, and increase customer satisfaction.
Furthermore, it demonstrates how AIServiceOps can deliver substantial cost savings, such as reducing
labor costs and minimizing downtime.
DESIGNING A FRAMEWORK FOR ENHANCING THE ONLINE KNOWLEDGE-SHARING BEHAVIOR OF ...IJMIT JOURNAL
The main objective of this paper is to identify the factors that influence academic staff's digital knowledgesharing behaviors in Ethiopian higher education. A structural equation model was used to validate the
research framework using survey data from 210 respondents. The collected data has been analyzed using
Smart PLS software. The results of the study show that trust, self-motivation, and altruism are positively
related to attitude. Contrary to our expectations, knowledge technology negatively affects attitude.
However, reward systems and empowerment by leaders are significantly associated with knowledgesharing intentions.Knowledge-sharing intention, in turn, was significantly related to digital knowledgesharing behavior. The contributions of this study are twofold. The framework may serve as a roadmap for
future researchers and managers considering their strategy to enhance digital knowledge sharing in HEI.
The findings will benefit academic staff and university administrations.The study will also help academic
staff enhance their knowledge-sharing practices.
BUILDING RELIABLE CLOUD SYSTEMS THROUGH CHAOS ENGINEERINGIJMIT JOURNAL
Cloud computing systems need to be reliable so that they can be accessed and used for computing at any
given point in time. The complex nature of cloud systems is the motivation to conduct research in novel
ways of ensuring that cloud systems are built with reliability in mind. In building cloud systems, it is
expected that the cloud system will be able to deal with high demands and unexpected events that affect the
reliability and performance of the system.
In this paper, chaos engineering is considered a heuristic method that can be used to build reliable cloud
systems. Chaos engineering is aimed at exposing weaknesses in systems that are in production. Chaos
engineering will help identify system weaknesses and strengths when a system is exposed to unexpected
knocks and shocks while it is in production.
Chaos engineering allows system developers and administrators to get insights into how the cloud system
will behave when it is exposed to unexpected occurrences.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
NETWORK MEDIA ATTENTION AND GREEN TECHNOLOGY INNOVATIONIJMIT JOURNAL
This paper will provide a novel empirical study for the relationship between network media attention and
green technology innovation and examine how network media attention can ease financing constraints. It
collected data from listed companies in China's heavy pollution industry and performed rigorous
regression analysis, in order to innovatively explore the environmental governance functions of the media.
It found that network media attention significantly promotes green technology innovation. By analyzing the
inner mechanism further, it found that network media attention can promote green innovation by easing
financing constraints. Besides, network media attention has a significant positive impact on green invention
patents while not affecting green utility model patents.
INCLUSIVE ENTREPRENEURSHIP IN HANDLING COMPETING INSTITUTIONAL LOGICS FOR DHI...IJMIT JOURNAL
Information System (IS) research advocates employing collaborative and loose coupling strategies to address contradictory issues to address diversified actors’ interests than the prescriptive and unilateral Information Technology (IT) governance mechanisms’, yet it is rarely depicting how managers employ these strategies in Health Information System (HIS) implementation, particularly in a resource-constrained setting where IS implementation activities have highly relied on multiple international organizations resources. This study explored how managers in resource-constrained settings employ collaborative IT governance mechanisms in the case of District Health Information System 2 (DHIS2) adoption with an interpretative case study approach and the institutional logic concept. The institutional logic concept was used to identify the major actors’ logics underpinning the DHIS2 adoption. The study depicted the importance of high-level officials' distance from the dominant systemic logic to consider new alternative, and to employ inclusive IT governance mechanisms which separated resource from the system that facilitated stakeholders’ collaboration in DHIS2 adoption based on their capacity and interest.
DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEMIJMIT JOURNAL
With an increasing number of extreme events and complexity, more alarms are being used to monitor
control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable
actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It
is important to have a rigid surveillance that should guarantee protection from any sought of hazard.
Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these
CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired
off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with
a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing
images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and
a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on
providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO
algorithm, it divides an image from the video into grid system and each grid detects an object within itself
MULTIMODAL COURSE DESIGN AND IMPLEMENTATION USING LEML AND LMS FOR INSTRUCTIO...IJMIT JOURNAL
Traditionally, teaching has been centered around classroom delivery. However, the onslaught of the
COVID-19 pandemic has cultivated usage of technology, teaching, and learning methodologies for course
delivery. We investigate and describe different modes of course delivery that maintain the integrity of
teaching and learning. This paper answers to the research questions: 1) What course delivery method our
academic institutions use and why? 2) How can instructors validate the guidelines of the institutions? 3)
How courses should be taught to provide student learning outcomes? Using the Learning Environment
Modeling Language (LEML), we investigate the design and implementation of courses for delivery in the
following environments: face-to-face, online synchronous, asynchronous, hybrid, and hyflex. A good
course design and implementation are key components of instructional alignment. Furthermore, we
demonstrate how to design, implement, and deliver courses in synchronous, asynchronous, and hybrid
modes and describe our proposed enhancements to LEML.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
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Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
DESIGNING A RECOMMENDER SYSTEM BASED ON SOCIAL NETWORKS AND LOCATION BASED SERVICES
1. International Journal of Managing Information Technology (IJMIT) Vol.4, No.4, November 2012
DOI : 10.5121/ijmit.2012.4404 41
DESIGNING A RECOMMENDER SYSTEM BASED ON
SOCIAL NETWORKS AND LOCATION BASED
SERVICES
Fatemeh Khoshnood, Mehregan Mahdavi and Maedeh Kiani sarkaleh
Department of Computer Engineering, University of Guilan, Rasht, Iran
khoshnood49@yahoo.com, mahdavi@guilan.ac.ir, maedehkiani@gmail.com
ABSTRACT
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable
frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and
preferences in a suggestion provided using just place-based services. Current generation of place-based
services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of
using social recommender systems was discussed which contains capability of identifying user’s interests
and preferences and based on them and user’s current place, it offers some suggestions. Social
recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible
to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can
acquire their favorite information and services.
KEYWORDS
Recommender System, Social Networks, Location based services, Mobile Device,
1. INTRODUCTION
Mobile devices have removed the location limitation such that one can personalize and suggest
content in suitable form to him or her while considering the environment where the person is. For
example, the tool can be used to obtain updated information about sightseeing around the world.
The information can be shared with other users interactively. There are certain challenges in this
area such as lack of an efficient model, diversity of information and the reality that the
information is not collected in a central server [1].
Personalization process is fulfilled using the information obtained from learner. On the whole, the
goal for personalization is to suggest a collection of comprehensive activities taking into
consideration factors such as location, user preferences and interest and so on [2]. Personalization
is performed through two methods: adjusting the learning services with learner’s features such as
learning methods, demands, situations, efficiencies, preferences and criteria; learning system
conformation with learner’s surroundings. Interrelation between user and user with the nearest
neighborhood algorithm is used by most personalized systems. User-user interrelation is resulted
from statistics and figures which signify the interrelations between variants and it is used to
measure the model efficiency. In recommender systems, the interrelation is used to measure the
rate of similarities between two users and to identify the users who are expected to be
categorized.
2. International Journal of Managing Information Technology (IJMIT) Vol.4, No.4, November 2012
42
There are just a few systems that use a combination of spatial data and social data on web like
social networks, web page visits by users or applying search engines simultaneously. Biancalana
et al [2] discussed the idea of such compound system. The system designed in [3] uses the
information which is collected based on user’s preferences and its content information includes
location, elapsed time in the distance, the direction of the selected path, personal preferences and
various user systems. Buchman and Hinze designed a system which suggested information about
the sightseeing to tourists [4] and its content information included user location, present time,
interests and such information about sightseeing as their locations, types and similarities. Other
system called Cyber guide has been designed for tourism industry [5]. This system acts as human
guide and uses PDA and GPS to give service to tourists in a site. Other system designed in [1],
called Murshid uses Google Map as a visual guide. It is a content intelligent system which uses
user location information, his or her interests, job and time schedule.
New generation of place-based systems do not provide users with personalized suggestions,
rather, based on the distance from where they are, they just offer suggestions close to interests. In
order to solve this problem, the idea of using recommender social systems was discussed. Those
systems contain capability of realizing user interests and preferences and based on them
considering user current place, they provide some suggestions. In systems which are based on
user preference place, the data required or points related to the content being used e.g. day of the
week, time, weather, user activity and transfer tool are not provided. Therefore, users become
afloat in suggestions which can even be of no interest for them and there is a possibility that this
suggestion results in user’s dissatisfaction. In mobile devices, due to limitations like; small
screen, limited input and things like that, the problem is intensified. Many investigations have
been performed about content to suggest suitable services to users and certain recommender
systems are introduced in brief in table 1.
MIT Media
Lab
National Tsing
Hua Univ.
Telematica
Institute
Murshid
Recommendation
type
Restaurant Restaurant
Travel
information
Tourism industry
Context
information
Location
Location, Time,
Weather
Location, Time,
Weather,
Shopping list,
Schedule
Location, profile,
Time, user
interaction
Recommendation
method
Interaction
between 2
agents
Search after
requesting
Variable
prediction strategy
reasoning engine,
J2ME
Device PDA, GPS Pocket PC, GPS
Mobile phone,
GPS
Mobile phone,
GPS, Google map
Table 1: some existing context-aware recommender systems
The model suggested in this article shows the process of providing recommendations for the user,
considering spatial factors and user’s personal information. In a touristic place, suggested model
can be used to find entertainment places like; parks and restaurants or art museums and concerts
held in that area.
Article’s structure is as follows: social networks are defined and analyzed in second section.
Section 3 discusses place-based services. Suggested model is considered in section 4. Section 5
considers evaluations and conclusion is contained in section 6.
3. International Journal of Managing Information Technology (IJMIT) Vol.4, No.4, November 2012
43
2. SOCIAL NETWORK
2.1. Social Network definition
A social network is defined as a social structure of people having relationship based on casual
interests e.g. friendship and honesty [6]. Social network system focuses on the structure and
identification of on-line social networks for those who share their interests and activities or those
who are interested in browsing others’ interests and activities. These networks, first, are used in
order to making friends and sharing ideas among members but today they are used in order to do
business and data sharing. Of course as the time passes, two business and friendly environments
are combined and change into compound environments [7 & 8].
Generally speaking, based on kind of their application, social network environments are divided
into following groups:
1. Friendly environments where people mostly chat or share ideas. These groups are formed
in order to make friendships and share photos and videos among friends for free.
2. Business environments where users perform business tasks as sellers or costumers. For
sellers, these groups are formed due to monetary benefits and for costumers for supplying
needs.
3. General environments which are formed in order to discuss and share information and
create knowledge as well.
4. Compound environment where there is a combination of friendly environment and
business or scientific environment users. As the time passes two friendly and business
environments are combined and change into a compound one [8].
Social networks are reliable due to their experiences, understanding values and needs [9]. For
example, friends chat about restaurants and cinemas. This point about social networks contains
two important potential benefits for advertising as well [10]:
• The advertisement displayed through social network can be considered as more reliable.
Actually, people prefer to rely on recommendations provided by their friends and
neighbors because human nature is more interested in what a friend buys, not an
unknown person, and it is likely that the user relies on a friend’s ideas and is influenced
by his/her actions.
• Potentially, social networks allow organizations to acquire valuable information about
users through observing their activities and this property of social network results in an
improvement of advertisement effects.
2.2. Social Network analysis
The title Social network analysis includes recording and measuring the relationships and events
among individuals, organizations and basically any other identity containing a capability of data
and knowledge process. Nodes in this network are individuals and groups and its edges are their
relationships. Social network analysis includes visual and formal analysis of human relationship.
Web and pages existing in it are an example of social network. Actually, pages can be considered
as nodes and links among them as edge among these nodes. On the other side, as new generation
of webs appeared and considering their main factors i.e. weblogs and wikis, the importance of
social networks in web is now higher.
4. International Journal of Managing Information Technology (IJMIT) Vol.4, No.4, November 2012
44
Through theoretical rules, methods and related researches, social networks analysis has
transformed from an implicit industry into an analytical route for paradigms. Analytical proofs
evaluate all things from whole to component, structure to relations and individuals, from manner
to behavior of all through networks where all courses including special relationship among
population are defined or they consider individual networks which include courses like; private
societies acquired by special individuals [11].
Recently, much attraction and interest has been observed in social network analysis among data
mining groups. Its main motivation is exploitation, recognition and awareness of values collected
concerned with users’ social behavior in on-line environments. Data mining techniques when
analyzing social network data especially for massive data collections are considered as useful
which are not controllable through traditional methods. This section provides an introduction to
important issue of data mining in social network analysis and research routes review where even
primitive application of data mining techniques results in a considerable statistical improvement
of reaction accuracy throughout cyber community [6].
Social networks have been used for evaluating the quality of mutual effects within systems and
describing lots of informal connections which connect administrators to each other and in this
regard, they cover individual relationships among employees in different organizations very well.
These networks play a crucial role in commercial success and job achievements. They provide
paths for companies to collect information, avoid competition and even collude with each other to
adjust prices and policies [12].
3. LOCATION BASED SERVICES
Location-based services are value added ones in which position information is used to present
diverse and interesting services to user including emergency services, vehicle navigation systems,
tourist broadcasting services, searching in country- wide or urban sites data banks using map.
Geographic Position System (GPS) works based on satellite tracking and allows user to locate
every point on the globe accurately. Mobile learning can be conducted through tracking by GPS.
The GPS data can define three concepts: users, locations and activities in a certain site [13].
Another facility is communication network (BSC and BTS).BTS is responsible for exchanging
radio waves with mobile unit as well as control and exchange data with BSC. BTS consists of
independent transceivers, providing aerial and radio connection with mobile unit. BTS is the
smallest unit of service provider in mobile radio network, supporting the certain region of the
network called cell. BTS is designed to support higher radio coverage in roads or where the signal
is weak and to meet traffic needs and its regular range in a smooth region is 30-35 KMs but it is
20 KMs in practice.
Radio Frequency Identification (RFID) is one of modern tracking technologies to locate user.
This system identifies mobile and static objects by radio waves. In this new method, certain tags
are used to store data. The data would be restored when needed [14]. The smart tag can be
embedded inside electronic and non electronic products, animals and human body. The tag can be
identified via transceiver waves in any time and place. RFID is now used for tracking vehicles,
people, objects, security, transaction register and so on.
Google Map is another method in which the map can be efficiently downloaded and stored for
future use. In its new versions, maps can be illustrated two and three dimensionally and it is
possible to search the locations, to mark the interested points on the map, to give support for such
services as Street View and My Tracks and audio guide.
5. International Journal of Managing Information Technology (IJMIT) Vol.4, No.4, November 2012
45
4. THE SUGGESTED MODEL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a
suitable frame considering individual’s location and present it. Yet, it is not possible to consider
user’s interests and preferences in a suggestion provided using just place-based services. Current
generation of place-based services do not provide users with personalized suggestions, instead
they just offer suggestions close to interests based on users distance from the place where they
are. In order to solve this problem, the idea of using social recommender systems was discussed
which contains capability of identifying user’s interests and preferences and based on them and
user’s current place, it offers some suggestions.
In place-based systems, user’s preferences, its required information or dimensions related to the
content used e.g. day of the week, weather, time, user’s activity and transfer tool are not
presented. Therefore, user becomes afloat in suggestions that may even be of no interest for
him/her and there is a possibility that this suggestion result in dissatisfaction for user. The
problem gets worse in mobile devices due to limitations like; small screen, limited input and
things like that. Figure 1 shows the process of recommending a visitor a new place to visit.
Figure 1. process of suggesting new place to visit
Social recommender systems are a combination of social data on web like; user’s social networks
and spatial information. Because user’s information include personal information and interests in
social network sites, considering user’s current location and the information existing in social
network data base, it is possible to provide user with a suitable suggestion. Through this method
users’ interaction decreases and they can acquire their favorite information and services.
Recommender system
User Mobile
Device
Social Network
Data Base
User Location
Figure 2. Spam traffic sample
Figure 2 shows the process of suggesting to a user considering spatial factors and user’s personal
information. Suggested model can be applied in a touristic area in order to find recreational places
6. International Journal of Managing Information Technology (IJMIT) Vol.4, No.4, November 2012
46
e.g. parks, restaurants or to find art museums or concerts held in that area. These steps are
discussed in the following:
One valuable capability of recommender systems is applying social network. Applying social
information on the web like; social network, visiting web pages by user and manner of search
engines in order to personalize content is very important for the user. Social network data base
includes issues like; age, job, skill and expertise, favorite food, favorite entertaining places …
people can discuss their ideas about different issues and share them with each other in these
networks. For example, if a person has visited a special place, he/she can discuss it his/her
personal page and others give ideas.
User’s location can be measured by technologies like; GPS, RFID or Google map. If this location
is not defined automatically, user can declare the location manually. In suggested model Google
map is used to identify user’s location. This software is a very efficient map with a capability of
downloading it for considered place, saving and keeping it for when map is needed.
User plays a crucial role in system and its information exists in social network data base. So it can
login a system using mobile device and is identified for the system through this method. The task
is done by entering a username and password in order to distinguish users as well. The method
used in recommender system is collaborative grouping. The main idea in this article is that similar
users receive similar services, i.e. if two users have similar interests they are placed in one group.
Recommender system provides a suitable suggestion using contents existing in data base and
conforming it to user’s location.
5. CONCLUSIONS
Personalization process is fulfilled using the information obtained from learner. On the whole, the
goal for personalization is to suggest a collection of comprehensive activities taking into
consideration factors such as location, user preferences and interest and so on. New generation of
place-based systems do not provide users with personalized suggestions, rather, based on the
distance from where they are, they just offer suggestions close to interests. In order to solve this
problem, the idea of using recommender social systems was discussed. Those systems contain
capability of realizing user interests and preferences and based on them considering user current
place, they provide some suggestions. The model suggested in this article shows the process of
providing recommendations for the user, considering spatial factors and user’s personal
information. In a touristic place, suggested model can be used to find entertainment places like;
parks and restaurants or art museums and concerts held in that area.
REFERENCES
[1] A. Echtibi, M. J. Zemerly, and J. Berri, (2009) "A Service-Based Mobile Tourist Advisor",
International Journal of Computer Information Systems and Industrial Management Applications
(IJCISIM), Vol.1, pp.177-187.
[2] A. K. Dey, and G. B. Abowd, (2001) "A Conceptual Framework and a Tool Kit for Supporting The
rapid Prototyping of Context aware Application", Journal Human-Computer Interaction, Vol. 16 Issue
2, pp. 97-166.
[3] C. Biancalana, F. Gasparetti, A. Micarelli, G. Sansonetti, (2011) "Social Tagging for Personalized
Location-Based Services", Workshop SRS’11, Hangzhou, China.
[4] A. Pashtan, R. Blattler, A. Heusser, and P. Scheuermann, (2003) "CATIS: A Context-Aware Tourist
Information System", Proceedings of the 4th International Workshop on Mobile Computing, Rostok,
June, pp.1-8.
7. International Journal of Managing Information Technology (IJMIT) Vol.4, No.4, November 2012
47
[5] A. Hinze, and G. Buchanan, (2005) "Context-awareness in Mobile Tourist Information Systems:
Challenges for User Interaction", Int. Workshop on Context in Mobile HCI, Salzburg.
[6] A. Garcia-Crespo, et al., (2009) "SPETA: Social pervasive e-Tourism advisor", Telematics and
Informatics, Vol. 26, pp. 306-315.
[7] J. Srivastava, (2008) "Data Mining for Social Network Analysis", Intelligence and Security
Informatics (ISI), IEEE International Conference on Advances in Social Networks Analysis and
Mining, Taipei.
[8] Y. Qiao, (2008) "Social networks and E-commerce", TKK, T-110.5190 Seminar on Internetworking.
[9] G. Swamynathan, C. Wilson, B. Boe, K. Almeroth, and B. Y. Zhao, (2008) "Do Social Networks
Improve e-Commerce? A Study on Social Marketplaces", ACM WOSN’08, Seattle, Washington,
USA.
[10] E. K. Clemons, (2007) "The future of advertising and the value of social networks", Wharton ISE
Blog, pp. 1–16.
[11] M. Karimzadehgan, M. Agrawal, C. X. Zhai, (2009) "Towards Advertising on Social Networks",
Proceedings of the ACM SIGIR workshop on Information Retrieval and Advertising(IRA), Boston,
MA, USA.
[12] W. Barry, and S. D. Brekowitz, (1998) "Social Structure: A Network Approach", Cambridge
University Press, page112.
[13] W. Standly, K. foust, (1994) "Social Network Analysis: Methods and Applications", Cambridge
University Press, page 48.
[14] V. W. Zheng, B. Cao, Y. Zheng, X. Xie, Q. Yang, (2010) "Collaborative Filtering Meets Mobile
Recommendation: A User-centered Approach", Association for the Advancement of Artificial
Intelligence (www.aaai.org).