Social search interfaces aim to improve information seeking through collaboration. Three studies evaluated FeedMe, SearchTogether, and Coagmento. FeedMe allowed one-click link sharing but raised privacy concerns without public knowledge triggers. SearchTogether supported large group collaboration but users wanted more real-time features. Coagmento effectively supported group awareness but received low marks for personal awareness. Overall, more research is needed on privacy protections within social search interfaces to fully realize their benefits while respecting user privacy.
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
AN EXTENDED HYBRID RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES MINING IN DI...csandit
Social groups in the form of different discussion forums are proliferating rapidly. Most of these
forums have been created to exchange and share members’ knowledge in various domains.
Members in these groups may need to use and retrieve other members’ knowledge. Therefore,
recommender systems are one of the techniques which can be employed in order to extract
knowledge based on the members’ needs and favorites. It is noteworthy that not only the users’
comments and posts can have valuable information, but also there are some other valuable
information which can be obtained from social data; moreover, it could be extracted from
relations and interactions among users. Hence, association rules mining techniques are one of
the techniques which can be applied in order to extract more implicit data as input to the
recommender system. Our objective in this study is to improve the performance of a hybrid
recommender system by defining new hybrid rules. In this regard, for the first time, we have
defined new hybrid rules by considering both users and posts’ content data. Each of the defined
rules has been examined on an asynchronous discussion group in this study. In addition, the
impact of the defined rules on the precision and recall values of the recommender system has
been examined. We found that according to this impact, a classification of the defined rules can
be considered and a number of weights can be assigned to each rule based on their impact and
usability in the specific domain or application. It is noteworthy that the results of the
experiments have been promising.
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.
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
AN EXTENDED HYBRID RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES MINING IN DI...csandit
Social groups in the form of different discussion forums are proliferating rapidly. Most of these
forums have been created to exchange and share members’ knowledge in various domains.
Members in these groups may need to use and retrieve other members’ knowledge. Therefore,
recommender systems are one of the techniques which can be employed in order to extract
knowledge based on the members’ needs and favorites. It is noteworthy that not only the users’
comments and posts can have valuable information, but also there are some other valuable
information which can be obtained from social data; moreover, it could be extracted from
relations and interactions among users. Hence, association rules mining techniques are one of
the techniques which can be applied in order to extract more implicit data as input to the
recommender system. Our objective in this study is to improve the performance of a hybrid
recommender system by defining new hybrid rules. In this regard, for the first time, we have
defined new hybrid rules by considering both users and posts’ content data. Each of the defined
rules has been examined on an asynchronous discussion group in this study. In addition, the
impact of the defined rules on the precision and recall values of the recommender system has
been examined. We found that according to this impact, a classification of the defined rules can
be considered and a number of weights can be assigned to each rule based on their impact and
usability in the specific domain or application. It is noteworthy that the results of the
experiments have been promising.
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.
IJRET : International Journal of Research in Engineering and TechnologyImprov...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
AN INTEGRATED RANKING ALGORITHM FOR EFFICIENT INFORMATION COMPUTING IN SOCIAL...ijwscjournal
Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...inventionjournals
The problem of web search time complexity and accuracy has been visited in many research papers, and the authors discussed many approaches to improve the search performance. Still the approaches does not produce any noticeable improvement and struggles with more time complexity as well. To overcome the issues identified, an efficient multi mode conceptual clustering algorithm has been discussed in this paper, which identifies the similar interested user groups by clustering their search context according to different conceptual queries. Identified user groups are shared with the related conceptual queries and their results to reduce the time complexity. The multi mode conceptual clustering, performs grouping of search queries and users according to number of users and their search pattern. The concept of search is identified by using Natural language processing methods and the web logs produced by the default web search engines. The author designed a dedicated web interface to collect the web log about the user search and the same data has been used to cluster the social groups according to number of conceptual queries. The search results has been shared between the users of identified social groups which reduces the search time complexity and improves the efficiency of web search in better manner
APPLYING THE AFFECTIVE AWARE PSEUDO ASSOCIATION METHOD TO ENHANCE THE TOP-N R...ijnlc
Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class of information filtering systems that seeks to predict how close is the match of the user’s preference to a recommended item. A common approach for making recommendations for a user group is to extend Personalized Recommender Systems’ capability. This approach gives the impression that group recommendations are retrofits of the Personalized Recommender Systems. Moreover, such an approach not taken the dynamics of group emotion and individual emotion into the consideration in making top-N recommendations. Recommending items to a group of two or more users has certainly raised unique challenges in group behaviors that influence group decision-making that researchers only partially understand. This study applies the Affective Aware Pseudo Association Method in studying group formation and dynamics in group decision making. The method shows its adaptability to group's moods change when making recommendations.
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.
We the humans are surrounded with immense unprecedented wealth of information which are available as documents, database or other resources. The access to this information is difficult as by having the information it is not necessary that it could be searched or extracted by the activity we are using. The search engines available should be also customized to handle such queries, sometime the search engines are also not aware of the information they have within the system. The method known as keyword extraction and clustering is introduced which answers this shortcoming by spontaneously recommending documents that are related to users’ current activities. When the communication takes place the important text can be extracted from the conversation and the words extracted are grouped and then are matched with the parts in the document. This method uses Natural Language Processing for extracting of keywords and making the subgroup that is a meaningful statement from the group, another method used is the Hierarchical Clustering for creating clusters form the keywords, here the similarity of two keywords is measured using the Euclidean distance. This paper reviews the various methods for the system.
Expectations for Electronic Debate Platforms as a Function of Application DomainIJERA Editor
Electronic debate (or commenting) platforms are used with many types of online applications, as a way to engage the users or to provide enhancements, e.g., based on some type of collaborative filtering [1], [2]. The applications enhanced with such debate platforms range widely : news, products, sport, religion, politics, etc. Therefore, the emerging question is whether it is possible to make one electronic debate mechanism good for all applications, and whether the studies on the success of a debate mechanism in one domain do automatically apply to other application domains. Here we compare two traditional application domains of electronic debate platforms: product evaluation and commented news. We exploit the fact that most users are very familiar with both types of such applications, and therefore surveys can be designed to gauge reliably subtle differences between expectations and properties of these domains. Based on over 1000 responses to surveys described here, we are able to report statistically significant differences between the user behavior and expectations in the studied domains.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
An Access Control Model for Collaborative Management of Shared Data in OSNSIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
ANALYSIS OF GAMIFICATION ELEMENTS TO EXPLORE MISINFORMATION SHARING BASED ON ...ijseajournal
Gamification elements provide apersonal drive to urge user experience, emotion, fun, and engagement, positively or negatively. These gamification elements mayhave beenunintentionallyemployedthroughthe designand implementation processof social media platforms toencourage users’ behaviour towards misinformationsharing. This study intends to answer the subsequent question” What are the mostly used gamification elements that couldpossibly encourage usersto share misinformation on social media platforms?”. The study empirically investigatesthe usage of gamification elements and their relation to U&G theorywith 286 participants. The results indicated that gamification elements usage scored highwith regard tothe self-expression perspective (frequency=216), as well as theinteraction& collaborations perspective (frequency=198). whereas, the information seeking perspective scored low (frequency=59) and leaderboard were the least usage(frequency=43). The results may be useful to guide software engineering, developers, GUI specialists to cater for design elements settings and their possible negative effects in social media contexts.
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.
Cottage planning has become an important topic in part because of recent changes in the law concerning property tax uncapping, but more importantly because of the financial and emotional havoc that failure to plan can wreak on families. Often, failure to properly plan for passing down the family cottage results in lawsuits among future generations, sale of the cottage to a third party, and/or strained family relationships, to say the least.
Successor Liability Issues – Unemployment and COBRASmithhaughey
Attorney Jonathan J. Siebers tackles the subject of Unemployment Tax clearances and issues surrounding business transactions in the latest Dual Business Meeting hosted by the Michigan Business Brokers Association.
Takeaways:
The basics of unemployment successor liability
The UIA Forms
COBRA
IJRET : International Journal of Research in Engineering and TechnologyImprov...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
AN INTEGRATED RANKING ALGORITHM FOR EFFICIENT INFORMATION COMPUTING IN SOCIAL...ijwscjournal
Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...inventionjournals
The problem of web search time complexity and accuracy has been visited in many research papers, and the authors discussed many approaches to improve the search performance. Still the approaches does not produce any noticeable improvement and struggles with more time complexity as well. To overcome the issues identified, an efficient multi mode conceptual clustering algorithm has been discussed in this paper, which identifies the similar interested user groups by clustering their search context according to different conceptual queries. Identified user groups are shared with the related conceptual queries and their results to reduce the time complexity. The multi mode conceptual clustering, performs grouping of search queries and users according to number of users and their search pattern. The concept of search is identified by using Natural language processing methods and the web logs produced by the default web search engines. The author designed a dedicated web interface to collect the web log about the user search and the same data has been used to cluster the social groups according to number of conceptual queries. The search results has been shared between the users of identified social groups which reduces the search time complexity and improves the efficiency of web search in better manner
APPLYING THE AFFECTIVE AWARE PSEUDO ASSOCIATION METHOD TO ENHANCE THE TOP-N R...ijnlc
Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class of information filtering systems that seeks to predict how close is the match of the user’s preference to a recommended item. A common approach for making recommendations for a user group is to extend Personalized Recommender Systems’ capability. This approach gives the impression that group recommendations are retrofits of the Personalized Recommender Systems. Moreover, such an approach not taken the dynamics of group emotion and individual emotion into the consideration in making top-N recommendations. Recommending items to a group of two or more users has certainly raised unique challenges in group behaviors that influence group decision-making that researchers only partially understand. This study applies the Affective Aware Pseudo Association Method in studying group formation and dynamics in group decision making. The method shows its adaptability to group's moods change when making recommendations.
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.
We the humans are surrounded with immense unprecedented wealth of information which are available as documents, database or other resources. The access to this information is difficult as by having the information it is not necessary that it could be searched or extracted by the activity we are using. The search engines available should be also customized to handle such queries, sometime the search engines are also not aware of the information they have within the system. The method known as keyword extraction and clustering is introduced which answers this shortcoming by spontaneously recommending documents that are related to users’ current activities. When the communication takes place the important text can be extracted from the conversation and the words extracted are grouped and then are matched with the parts in the document. This method uses Natural Language Processing for extracting of keywords and making the subgroup that is a meaningful statement from the group, another method used is the Hierarchical Clustering for creating clusters form the keywords, here the similarity of two keywords is measured using the Euclidean distance. This paper reviews the various methods for the system.
Expectations for Electronic Debate Platforms as a Function of Application DomainIJERA Editor
Electronic debate (or commenting) platforms are used with many types of online applications, as a way to engage the users or to provide enhancements, e.g., based on some type of collaborative filtering [1], [2]. The applications enhanced with such debate platforms range widely : news, products, sport, religion, politics, etc. Therefore, the emerging question is whether it is possible to make one electronic debate mechanism good for all applications, and whether the studies on the success of a debate mechanism in one domain do automatically apply to other application domains. Here we compare two traditional application domains of electronic debate platforms: product evaluation and commented news. We exploit the fact that most users are very familiar with both types of such applications, and therefore surveys can be designed to gauge reliably subtle differences between expectations and properties of these domains. Based on over 1000 responses to surveys described here, we are able to report statistically significant differences between the user behavior and expectations in the studied domains.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
An Access Control Model for Collaborative Management of Shared Data in OSNSIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
ANALYSIS OF GAMIFICATION ELEMENTS TO EXPLORE MISINFORMATION SHARING BASED ON ...ijseajournal
Gamification elements provide apersonal drive to urge user experience, emotion, fun, and engagement, positively or negatively. These gamification elements mayhave beenunintentionallyemployedthroughthe designand implementation processof social media platforms toencourage users’ behaviour towards misinformationsharing. This study intends to answer the subsequent question” What are the mostly used gamification elements that couldpossibly encourage usersto share misinformation on social media platforms?”. The study empirically investigatesthe usage of gamification elements and their relation to U&G theorywith 286 participants. The results indicated that gamification elements usage scored highwith regard tothe self-expression perspective (frequency=216), as well as theinteraction& collaborations perspective (frequency=198). whereas, the information seeking perspective scored low (frequency=59) and leaderboard were the least usage(frequency=43). The results may be useful to guide software engineering, developers, GUI specialists to cater for design elements settings and their possible negative effects in social media contexts.
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.
Cottage planning has become an important topic in part because of recent changes in the law concerning property tax uncapping, but more importantly because of the financial and emotional havoc that failure to plan can wreak on families. Often, failure to properly plan for passing down the family cottage results in lawsuits among future generations, sale of the cottage to a third party, and/or strained family relationships, to say the least.
Successor Liability Issues – Unemployment and COBRASmithhaughey
Attorney Jonathan J. Siebers tackles the subject of Unemployment Tax clearances and issues surrounding business transactions in the latest Dual Business Meeting hosted by the Michigan Business Brokers Association.
Takeaways:
The basics of unemployment successor liability
The UIA Forms
COBRA
AN EXTENDED HYBRID RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES MINING IN DI...cscpconf
Social groups in the form of different discussion forums are proliferating rapidly. Most of these forums have been created to exchange and share members’ knowledge in various domains.
Members in these groups may need to use and retrieve other members’ knowledge. Therefore, recommender systems are one of the techniques which can be employed in order to extract
knowledge based on the members needs and favorites. It is noteworthy that not only the users comments and posts can have valuable information, but also there are some other valuable information which can be obtained from social data; moreover, it could be extracted from relations and interactions among users. Hence, association rules mining techniques are one of the techniques which can be applied in order to extract more implicit data as input to the recommender system. Our objective in this study is to improve the performance of a hybrid
recommender system by defining new hybrid rules. In this regard, for the first time, we have defined new hybrid rules by considering both users and posts’ content data. Each of the defined rules has been examined on an asynchronous discussion group in this study. In addition, the impact of the defined rules on the precision and recall values of the recommender system has been examined. We found that according to this impact, a classification of the defined rules can
be considered and a number of weights can be assigned to each rule based on their impact and usability in the specific domain or application. It is noteworthy that the results of the
experiments have been promising
Integrated expert recommendation model for online communitiesst02IJwest
Online communities have become vital places for Web 2.0 users to share knowledg
e and experiences.
Recently, finding expertise user in community has become an important research issue. This paper
proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from
enormous contents and social network fe
atures. Vector space model is used to compute the relevance of
published content with respect
to a specific query while PageRank
algorithm is applied to rank candidate
experts. The experimental results sho
w that the proposed model is
an effective recommen
dation which can
guarantee that the most candidate experts are both highly relevant to the specific queries and highly
influential in corresponding areas
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.
FACILITATING VIDEO SOCIAL MEDIA SEARCH USING SOCIAL-DRIVEN TAGS COMPUTINGcsandit
Online video search or stream live on social media has become tremendous widespread and
speedy increased continuously in recent years. Most of the videos shared on social media are
aimed at the more number of views from audiences. What and how many videos the users
shared all around the world have created a great amount and varied videos and the other data
into Internet cloud’s database and even can be viewed as a kind of big data of digital contents.
This research is to present how to implement a social-driven tags computing (SDT) which can
be used to facilitate online video search on social media platforms
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A Literature Survey on Recommendation System Based on Sentimental Analysisaciijournal
Recommender systems have grown to be a critical research subject after the emergence of the first paper
on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems,
has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation
and classification of that research. Because of this, we reviewed articles on recommender structures, and
then classified those based on sentiment analysis. The articles are categorized into three techniques of
recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to
find out the research papers related to sentimental analysis based recommender system. To classify
research done by authors in this field, we have shown different approaches of recommender system based
on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in
recommender structures research, and gives practitioners and researchers with perception and destiny
route on the recommender system using sentimental analysis. We hope that this paper enables all and
sundry who is interested in recommender systems research with insight for destiny.
Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system based on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.
Social bookmarking system is a web-based resource sharing system that allows users to upload, share and
organize their resources i.e. bookmarks and publications. The system has shifted the paradigm of
bookmarking from an individual activity limited to desktop to a collective activity on the web. It also
facilitates user to annotate his resource with free form tags that leads to large communities of users to
collaboratively create accessible repositories of web resources. Tagging process has its own challenges
like ambiguity, redundancy or misspelled tags and sometimes user tends to avoid it as he has to describe
tag at his own. The resultant tag space is noisy or very sparse and dilutes the purpose of tagging. The
effective solution is Tag Recommendation System that automatically suggests appropriate set of tags to
user while annotating resource. In this paper, we propose a framework that does not depend on tagging
history of the resource or user and thereby capable of suggesting tags to the resources which are being
submitted to the system first time. We model tag recommendation task as multi-label text classification
problem and use Naive Bayes classifier as the base learner of the multilabel classifier. We experiment with
Boolean, bag-of-words and term frequency-inverse document frequency (TFIDF) representation of the
resources and fit appropriate distribution to the data based on the representation used. Impact of feature
selection on the effectiveness of the tag recommendation is also studied. Effectiveness of the proposed
framework is evaluated through precision, recall and f-measure metrics.
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.
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.
Classifying web users in a personalised search setup is cumbersome due the very nature of dynamism in
user browsing history. This fluctuating nature of user behaviour and user interest shall be well interpreted
within a fuzzy setting. Prior to analysing user behaviour, nature of user interests has to be collected. This
work proposes a fuzzy based user classification model to suit a personalised web search environment. The
user browsing data is collected using an established customised browser designed to suit personalisation.
The data are fuzzified and fuzzy rules are generated by applying decision trees. Using fuzzy rules, the
search pages are labelled to aid grouping of user search interests. Evaluation of the proposed approach
proves to be better when compared with Bayesian classifier.
1. Social Search Interfaces in Information Retrieval
Jennifer Kott
College of Information Science
and Technology, Drexel
University
Jmk376@drexel.edu
ABSTRACT Google and Microsoft have come out with two good collaboration
This paper examines social web search, collaborative interfaces interfaces which support social search. User studies from both of
tools and their role in seeking information. The literature these interfaces are reviewed in this paper. Study one looks at
referenced in this paper, is a combination of several studies on FeedMe, a plug in for Google. FeedMe is a behind the scenes way
user preference, habits and how users share information using of sharing content with friends by sharing links. [1]. FeedMe
collaborative interfaces. prompts users to share web links with friends and asks them their
opinion on what was shared. Another study tested the
Participates of the collaboration tool studies were chosen at SearchTogether a browser plug-in from Microsoft. [4].
random and paid for their responses. Feedback was given based SearchTogether is a real-time collaboration tool giving users the
on user experience and testing of software features. Collaboration opportunity to view fellow user’s searches and socialize with
software evaluated included: FeedMe, SearchTogether and them during the searching process.
Coagmento. Each study lasted a period of about two weeks. The
user habit models discussed includes: the Random Walk Model, The goal of each user study was to measure the products
Resource Recommendation Model, Tagging Model and Link effectiveness as a collaboration tool. Side benefits of each study
Sharing. were user suggestions on product enhancements which would
make collaboration easier. The first step to making improvements
To create better collaboration tools you need to evaluate the
on collaborative interfaces is to fully understand how social files
existing ones and identify where improvements can be made. In
sharing works. We do that by first examining user habits,
these studies, the users identified a few areas where tools could
preferences and users need to share information.
help with improving content sharing. For example, privacy
concerns were noted in the FeedMe study. Users made a 2. LITERATURE REVIEW
suggestion to help with the privacy issue. They requested that a Literature shows, user preferences play a key role in determining
public knowledge trigger be added to the software. What is how successful the social sharing will be. User preferences in the
unclear is what will happen if the public knowledge trigger fails. literature showed users shied away from the advanced search
Would the creators of the FeedMe tool be held liable? features in favor of smaller searches. It’s not clear why, but two
Creators of collaboration tools need to take things slow. theories point to anything from a lazy user to lack of knowledge
Additional studies need to be done comparing the benefits versus when searching. [4] The user tends to use multiple word queries to
legal implications of changing some of the collaboration tools. search for information or relays on others to find information for
Users may play a very important role in determining the them. On more advance searches users typical involve librarians.
collaboration tools of the future.
Identifying a user as lazy maybe a bit harsh, a more reasonable
Categories and Subject Descriptors explanation may point to the preparation of the user prior to
H5.2 . Information interfaces and presentation (e.g. HCI) engaging in the search. Users who take the time to plan, organize
and set goals prior to seeking information tend to have more
General Terms successful outcomes. [3] The figure below illustrates a three step
Design, Human Factors, Verification. process a user goes through when seeking information. We break
each step by; 1) purpose, 2) gathering of requirements and 3)
Keywords formulate representation. 68.7 % were self-motivated searches
Social link sharing, blogs, RSS, social search, navigational search, amd 31.3% were motivated by some type of external source. [3]
query, tagging, taxonomy, informational, user-centered, social
collaboration, personalization, data.
1. INTRODUCTION
The search for information has become more of a collaborative
effort. Methods of sharing information have evolved over the past
few years with WEB 2.0. Web users can not only submit content
but enhance it through personalization. A magnitude of
information is out on the internet for users to sift through, make
sense of, to find what is relevant. Studies into online behavior
show users will seek help from others when having difficulty Figure 1. Shows prepartion prior to seeking information. (Evans, 2009)
locating information online. A recommendation by another user
can aid in that search. Recently developed interfaces help to
support different types of searches.
2. 2.1 User Preferences by speeding up retrieval relevant content because it shorten the
User habits and preferences are only one aspect of understanding number of clicks needed to locate the content.
the user. Equally important is an understanding of what the user is This diagram shows how the random walk model works. Side (a)
searching for. The three types of searches discussed in the illustrates the synonym part of the model and side (b) illustrates
literature include navigational, transactional, and informational. the homographs using user preferences.
Literature suggests social search works best the informational type
search. [3]
In an information search, users are on a journey to seek out
relevant information. They do not follow a specific path to find
information and may not be familiar with the topic. This type of
behavior is known as forging. In forging for information users
start in one direction and may be led into another direction by a
simple click. In social search, users are influenced by their peers
often leading them different directions. Interfaces like CiteULike
and Del.icio.us help users gather the forged information. These
two systems are social tagging systems. Social tagging links the
user to a resource. Figure 2: Random walk model (Clements, 2009)
It’s important to note, a tagged resource does not mean
automatically equal a good resource. Users need to keep in mind,
a tag is a recommendation, and other factors may play into the 3.2 Resource-recommendation Model
reason why a resource was tagged. These factors may include
education and interests of tagger. When seeking information the The Resource-recommendation model examined the social
results need to be deemed relevant and creditable in order to be tagging behaviors and time and tagging behaviors of users. The
useful. study used tag and time behaviors to come up with ratings using a
dataset from CiteULike. The recommended resource model used
2.2 User Opinions tag-weight rating, time-weight rating and a tag time rating.
Research varies on the usefulness of tagged content but tagged Overlapping ratings combined to form a user similarity
content used in conjunction with user opinions carries more calculation. The output of the model is the recommended
weight. Rating systems built into interfaces allow users to rate the resource. The diagram below illustrates the framework of the
quality of the content. Content is put in to specific categories Resource-recommendation model.
based on each rating. Placing content into specific categories,
based on a rating given by a user, is still seen as subjective [2]
Content chosen based on a rating system is flawed, because the
rating is based on opinion rather that fact.
3. Tagging Models
A side benefit of social tagging and rating is building content.
LibraryThing keep track of books they have read, tag them and
rate them. Users can use the benefits of tag and rating to find
recommend reading on various subjects of interest. The study on
LibraryThing showed the parallel between number of tags and
number of web searches. [2] In LibraryThing, users tag content
for a variety of reasons. A tag can be used to describe content, a
location or a status. Tags are good for putting books in to specific
categories. Tagging is another way to help users find content.
3.1 Random Walk Model Figure 3. Framework: Resource recommendation Model (Zheng,
Vocabulary remains a hurdle in many tagging systems. Variations 2010)
in words, use of synonyms and homographs have a directly affect
on search results. Similar problems occur in plural forms of the
words. To overcome this hurdle, a studying using a method called
clustering. Clustering puts tags into groups, groups were 3.3. Link Sharing
determined by how closely they relate to each other. Link sharing is another form of sharing content. A survey [1]
The Random Walk Model looked at the outcomes of clustering conducted by 40 web users asked four basic questions on link
when synonyms were used. These clusters helped improve sharing: 1) What tools do you use to share content 2) How do you
outcomes. Shorter queries of four or less terms showed the best go about finding and reviewing new web content? 3) Which is the
results. strongest motivator when you share links? 4) Which is the biggest
concern you have when you share links?
To overcome the homograph hurdle evaluators used the same Findings of the survey show email as the number one method of
random walk model to pair up the query tag and the target user sharing content. Favorite ways of discovering new content were
preference. The matching of user preference to query tag worked, visiting favorite websites a few times a week and receiving URLs
via email from unknown recipients. What motivates a user to
3. share a link? Answer given most often, they thought the topic features in SearchTogether include thumbs up/down, peek and
might interest the other person. A respondent were uncertain on follow browsing and on-line chatting via a add/comment box. [5]
the links relevance to the other user but shared them anyway. A
main worry among users was the possibility of too much email The user study published by Morris & Horvitz, 2007b and
being forwarded to one person. evaluated by Wilson [5] highlighted four areas where users
wished to have better control over collaboration. In peeking and
User sharing habits showed a tendency to share link more following, users wanted to know who is peeking and who is
prevalent between friends over someone they did not know. The following them. Rights to see the same URL another user is
survey theorized this was because friends know the interests of currently viewing and being able to push pages to other users with
their friends the best. For example, if your friend liked to cook ease. Along with the flexibility, to edit and annotate any search
and you came across a recipe, you would be more likely to share summary pages.
the recipe with your friend over a stranger whose taste you are not
familiar with. 4.3 Coagmento
Coagmento a plug-in from Firefox helps remote workers
4. SOCIAL SEARCH communicate, search, share and organize information over the
Social search defined in the literature as social interactions with web. [6] There are several good collaboration tools inside of the
others [3] Interactions can be explicit or implicit, co-located or Cogmento plugin. Information collection tools help users create
remote, synchronous or asynchronous. (Evans, 2009) To get users annotations and save and remove webpages. To help with
engaged socially the collaboration software must be easy to use. collaboration a side panel is equipped with a chat window and a
The fact is, a user who needs to jump through hoops to share history of search engine queries, saved pages and snippets for
information won’t bother do it! In theory, a good collaboration users to exchange thoughts and ideas. [6]
tool gives user’s a variety of ways to communicate, share, search
and organize information. This study focused on awareness in CIS. It used 84 participates
from the University of North Carolina at Chapel Hill and
4.1 FeedMe measured three conditions; Contextual awareness, work space
To test this theory, a study conducted over a two week period of awareness and examined the workspace area provided for group
time followed 60 users of FeedMe. FeedMe prompts users to collaboration. Personal peripheral awareness measured how well
share web links with their friends and asks them to give feedback the interface supported user’s personal history including, saved
on content shared. The purpose of the study is to gain a better documents, snippets and queries. Group peripheral awareness
understanding of the user. What features do they like? What looked at the same thing as personal peripheral awareness but
features could be improved in the software? from a group level perspective.
A few key things were learned from the FeedMe study. Features A key outcome of the Coagmento awareness study showed the
users scored most favorable were the one-click thanks feature and design of the Coagmento interface supported group awareness for
the later instead of now feature. The one-click thanks feature is an synchronous collaboration the best. [6] The product received low
automated response to thank the person sending you the link. The marks in the area of personal awareness. Group users had no
later instead feature is a view into the receipts email box. If the problems keeping up on the status of projects. They had full
sender thinks the recipient already has too many links waiting to visibility into what each member of the project was working on at
be viewed, they can schedule the link to show up later. The later all times and were able to collaborate with them through multiple
instead feature scored favorable among users because it allowed phases of the project.
users to share information in a polite way as to not overwhelm the Reported as unfavorable under group awareness was the lack of
recipient. [1] real-time collaboration. Users suggested some type of shared
User privacy concerns were one alarming finding of the study. notepad workspace be added to help with the real-time
Users feed recommended topics into the system based system collaboration issues. [6] Coagmento was designed for
suggestions of each user’s interest. Sharing information collaboration in synchronous or asynchronous mode. To support
concerning a disease, could potentially tip off other users to a synchronous-remote collaboration some major changes would be
health issue. Suggestions on how to fix the privacy issue, called need made to Coagmento. Another suggestion s was some type of
for a trigger called public knowledge to be added to the interface. alert system when new information added from a fellow group
The public knowledge control would be set by the user. [1] Only member. An alert would be helpful in those situations when a user
the public knowledge topics deemed by each user would show up needed to pick up some critical information about a task or a
as their interests. Users would decide when a topic was safe to change in a project.
discuss and when. This approach sounds reasonable but it’s
unclear if users would really take the time to setup public
5. Research Gaps
knowledge triggers. Additional studies would need to be done in A look at the data from all three user group studies show gaps in
this area. some of the research. Privacy and users rights to privacy are
missing. We do not know if any of the interfaces tested do a good
4.2 SearchTogether job at protecting the user’s right to privacy. Are some interfaces
SearchTogether from Microsoft is effective for those who like to better than others when it comes to protecting the user’s privacy
scan for information or learn from information. It supports large or is the burden of protecting private information solely on the
group collaboration by using group queries histories and split shoulders of the user? This question remains unanswered. One
searching. SearchTogether is not a structured search tool. Instead user pointed to privacy concerns in the FeedMe study by
it’s for the user who is not sure what they are looking for. They suggesting the need for a public knowledge trigger. A public
could just be browsing for ideas or information. Collaborative knowledge trigger would aid in protecting privacy concerns of the
information sharer but only if the user the trigger. Automatic
4. privacy protection needs to be built in to all of the products for it the user to share information, it appears more work is needed in
to be useful. the area of protecting the user’s privacy. Until interfaces can
protect the user’s privacy the benefits of social search may only
Social searching does not mean users have a right to know all of be shared friends.
your private business. A check box in the interface can help
filtering what information you wish to share and when. Results 7. REFERENCES
from the Coagnento study had users request more real-time [1] Bernstein, M.S., Marcus, A. Karger, D. R. and Miller, R.C.
collaboration tools when working with remote users. Real-time (20100. Enhancing directed content sharing on the web.
collaboration could be seen as a major benefit when working on NewYork, N.Y : In Proceedings of the SIGCHI Conference
projects in a group. Unclear in the study is who made this on Human Factors in Computing Systems (CHI '10). ACM,
recommendation? Was it a user or did management request such a pp. 971-980. DOI=10.1145/1753326.1753470
tool as a way to keep tabs on off-site workers? A may give up http://doi.acm.org.ezproxy2.library.drexel.edu/10.1145/1753
some of their rights to privacy in a real-time collaboration 326.1753470
interface.
Similar privacy concerns are noted using the peek and follow
[2] Clements, M., de Vries, A.P., and Reindeers, M.J. T.( 2009).
features in SearchTogether. In SearchTogether users have rights
The influence of personalization on tag query length in social
to know who is peeking and who is following them does the user
media search. Information Processing & Management,
have rights to stop a user from peeking and following? If so are
Volume 46, Issue 4, July 2010, pp. 403-412Tavel, P. 2007.
the tools adequate to protect the user’s privacy?
Modeling and Simulation Design. AK Peters Ltd., Natick,
All three studies did a good job of asking users their which tools MA.
were useful to them and which ones could be improved. The [3] Evan, B.M. and Ed. H.Chi. 2009. An elaborated model of
social sharing of information requires a collaborative interface social search. Information Processing & Management,
which helps protect the user and their privacy. Needs will only Volume 46, Issue 6, November 2010, pp. 656-678.
continue to grow as more gadgets are invented to support social
search participation. [4] McDonnell, M. and Shiri, A.2011. Social search: A
taxonomy of, and a user-center approach to, social web
6. CONCLUSION search. Program: electronic library and information systems,
The information seeking behaviors of the users show content Vol. 45, Iss.: 1 pp. 6-28. Emerald Group Pub. Ltd. 0033-
sharing on the web is here to stay. This paper took a look at some 0337 DOI 10.1108/00330331111107376
of the collaborative interfaces used in social search on the web [5] Wilson, M. L. and Schraefel, M.C.(2010) Evaluating
and asked users to rate their effectiveness. Feedback from the user collaborative information-seeking interfaces with a search-
studies like the ones reviewed in this paper can help developers oriented inspection method and re-framed information
build better tools to share information. seeking theory. Information Processing & Management,
Volume 46, Issue 6, pp. 718-732
The other pieces of the social search puzzle include an http://www.sciencedirect.com/science/article/pii/S030645730
understanding of user habits. What prompts the user to share 9001125
information? Is it the tool, the subject matter or is it the
relationship with the other user? The links sharing study pointed [6] Shah, C. and Marchionini, G. (2010), Awareness in
to user relationship as the catalyst to sharing a link. Sharing a link collaborative information seeking. J. Am. Soc. Inf. Sci., 61:
with a friend was far more prevalent than sharing a link with a 1970–1986. doi: 10.1002/asi.21379
stranger. This finding was not surprising, because users are more
comfortable around friends and they know the interests of their [7] Nan Zheng, Qiudan Li, 2010. A recommender system based
friends the best. So why not share information with them? on tag and time information for social tagging systems,
If we want users to step outside of their comfort zone and share Expert Systems with Applications, Volume 38, Issue 4, April
information with strangers we need to build interfaces which 2011, pp. 4575-4587, ISSN 0957-4174,
support, ease of use, anticipate what a user is searching for and a 10.1016/j.eswa.2010.09.131.
way to protect the user from invasion of privacy. http://www.sciencedirect.com/science/article/pii/S095741741
0010882
This paper identifies a few gaps in the studies with regards to
user’s privacy. Developers have worked hard at adding features
supportive of collaboration. All with the aim to make it easier for