Cross-domain collaborative filtering (CDCF) is an evolving research topic in recommender systems. It aims to alleviate the data sparsity problem in individual domains by transferring knowledge among related domains. But it has an issue of user interest drift over time. Along with data sparsity, we should also consider the temporal domains to overcome user interest drift over time problem to predict more accurately as per the current user’s interest. This paper surveys few of the pilot studies in this research line and the methods of how to add the temporal domains in the recommender systems. The paper also proposes possible extensions of using temporal domains with different contexts in current timestamp.
An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniqueijcsit
Recommender systems provide useful recommendations to a collection of users for items or products that
might be of concern or interest to them. Several techniques have been proposed for recommendation such
as collaborative filtering, content-based, knowledge-based, and demographic filtering. Each of these
techniques suffers from scalability, data sparsity, and cold-start problems when applied individually
resulting in poor recommendations. This paper proposes an adaptive hybrid recommender system that
combines multiple techniques together to achieve some synergy between them. Collaborative filtering and
demographic techniques are combined in a weighted linear formula. Different experiments applied using
movieLen dataset confirm that the proposed adaptable hybrid framework outperforms the weaknesses
resulted when using traditional recommendation techniques.
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...IJDKP
Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant
contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity
logs and product items, accurate and efficient recommendation is a challenging computational task. This
paper introduces a new soft hierarchical clustering algorithm - Fuzzy Hierarchical Co-clustering (FHCC)
algorithm, and applies this algorithm to detect user-product joint groups from users’ behavior data for
collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be
analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of
applications according to the accessibility of data sources by carefully adjust the weights of different data
sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product
co-clusters. The results show that our proposed approach provide more meaningful recommendation
results, and outperforms existing item-based and user-based collaborative filtering recommendations in
terms of accuracy and ranked position.
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.
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.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
A recommender system(RS) is an information filtering system that recommends the related suggestions as per the end users requirement. Applications of RS include recommendation of movies, music, serials, books, documents, websites, tourist places etc.
Benefits of RS: RSs are beneficial to both service providers and to the users. RSs reduce transaction costs of finding and selecting items.& RSs help in decision making. The proposed work DEMOGRAPHY BASED HYBRID SYSTEM FOR MOVIE RECOMMENDATIONS highlights the combination of collaborative, content based & demographic filtering to recommend movies to the end user. The model uses SVD++ technique available in Surprise Python library and achieves the MSE of 0.92 which is comparatively less than the other techniques.
A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on...Editor IJCATR
Recommender systems are gaining a great popularity with the emergence of e-commerce and social media on the internet. These recommender systems enable users’ access products or services that they would otherwise not be aware of due to the wealth of information on the internet. Two traditional methods used to develop recommender systems are content-based and collaborative filtering. While both methods have their strengths, they also have weaknesses; such as sparsity, new item and new user problem that leads to poor recommendation quality. Some of these weaknesses can be overcome by combining two or more methods to form a hybrid recommender system. This paper deals with issues related to the design and evaluation of a personalized hybrid recommender system that combines content-based and collaborative filtering methods to improve the precision of recommendation. Experiments done using MovieLens dataset shows the personalized hybrid recommender system outperforms the two traditional methods implemented separately.
A Study of Neural Network Learning-Based Recommender Systemtheijes
A recommender system sorts and recommends the information which meets personal preferences among a huge amount of data provided by e-commerce. In particular, collaborative filtering (CF) is the most widely used technique in these recommendation systems. This method finds neighboring users who have similar preferences with particular users and recommends the items preferred by the former. This study proposes a neural network learning model as a new technique to find neighboring users using the collaborative filtering method. This kind of neural network learning model takes care of a sparseness problem during the analysis stage among those related with target users. The proposed method was tested with MovieLens data sets, and the results showed that precision improved by 6.7%.
An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniqueijcsit
Recommender systems provide useful recommendations to a collection of users for items or products that
might be of concern or interest to them. Several techniques have been proposed for recommendation such
as collaborative filtering, content-based, knowledge-based, and demographic filtering. Each of these
techniques suffers from scalability, data sparsity, and cold-start problems when applied individually
resulting in poor recommendations. This paper proposes an adaptive hybrid recommender system that
combines multiple techniques together to achieve some synergy between them. Collaborative filtering and
demographic techniques are combined in a weighted linear formula. Different experiments applied using
movieLen dataset confirm that the proposed adaptable hybrid framework outperforms the weaknesses
resulted when using traditional recommendation techniques.
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...IJDKP
Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant
contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity
logs and product items, accurate and efficient recommendation is a challenging computational task. This
paper introduces a new soft hierarchical clustering algorithm - Fuzzy Hierarchical Co-clustering (FHCC)
algorithm, and applies this algorithm to detect user-product joint groups from users’ behavior data for
collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be
analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of
applications according to the accessibility of data sources by carefully adjust the weights of different data
sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product
co-clusters. The results show that our proposed approach provide more meaningful recommendation
results, and outperforms existing item-based and user-based collaborative filtering recommendations in
terms of accuracy and ranked position.
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.
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.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
A recommender system(RS) is an information filtering system that recommends the related suggestions as per the end users requirement. Applications of RS include recommendation of movies, music, serials, books, documents, websites, tourist places etc.
Benefits of RS: RSs are beneficial to both service providers and to the users. RSs reduce transaction costs of finding and selecting items.& RSs help in decision making. The proposed work DEMOGRAPHY BASED HYBRID SYSTEM FOR MOVIE RECOMMENDATIONS highlights the combination of collaborative, content based & demographic filtering to recommend movies to the end user. The model uses SVD++ technique available in Surprise Python library and achieves the MSE of 0.92 which is comparatively less than the other techniques.
A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on...Editor IJCATR
Recommender systems are gaining a great popularity with the emergence of e-commerce and social media on the internet. These recommender systems enable users’ access products or services that they would otherwise not be aware of due to the wealth of information on the internet. Two traditional methods used to develop recommender systems are content-based and collaborative filtering. While both methods have their strengths, they also have weaknesses; such as sparsity, new item and new user problem that leads to poor recommendation quality. Some of these weaknesses can be overcome by combining two or more methods to form a hybrid recommender system. This paper deals with issues related to the design and evaluation of a personalized hybrid recommender system that combines content-based and collaborative filtering methods to improve the precision of recommendation. Experiments done using MovieLens dataset shows the personalized hybrid recommender system outperforms the two traditional methods implemented separately.
A Study of Neural Network Learning-Based Recommender Systemtheijes
A recommender system sorts and recommends the information which meets personal preferences among a huge amount of data provided by e-commerce. In particular, collaborative filtering (CF) is the most widely used technique in these recommendation systems. This method finds neighboring users who have similar preferences with particular users and recommends the items preferred by the former. This study proposes a neural network learning model as a new technique to find neighboring users using the collaborative filtering method. This kind of neural network learning model takes care of a sparseness problem during the analysis stage among those related with target users. The proposed method was tested with MovieLens data sets, and the results showed that precision improved by 6.7%.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
A LOCATION-BASED RECOMMENDER SYSTEM FRAMEWORK TO IMPROVE ACCURACY IN USERBASE...ijcsa
Recommender systems are utilized to predict and recommend relevant items to system users. Item could be
in any forms such as documents, location, movie and articles. The mechanism of recommender system is
based on examination which includes users’ behaviors, item ratings, various logs (e.g. user’s history log)
and, social connections. The main objective of the examination is to predict items which have great potential to be liked by users. Although, traditional recommender systems have been very successful to predict what user might like, they did not take into consideration contextual information such as users’
location. In this paper, we propose a new framework with the aim of enhancing accuracy of recommendations in user-based collaborative filtering by considering about users’ locations.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
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.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
IMPROVING COLLABORATIVE RECOMMENDATION VIA USER-ITEM SUBGROUPSNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Improving Service Recommendation Method on Map reduce by User Preferences and...paperpublications3
Abstract: Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, to address the above challenges and presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative filtering algorithm is adopted to generate appropriate recommendations.Keywords: recommender system, user preference, keyword, Big Data, mapreduce, Hadoop.
Title: Improving Service Recommendation Method on Map reduce by User Preferences and Reviews
Author: Dayanand Bhovi, Mr. Ashwin Kumar
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
Improvement in Error Resilience in BIST using hamming codeIJMTST Journal
In the current scenario of IP core based SoC, to test the CUT we need to communication link between Circuit Under Test and ATPG, so before applying to actual DUT. If there is a problem with this link, there may be a lip in bit of test data. Compared to original test data, if there is a bit lip in the original data, the codeword may change and hence the decompressed data will have a large number of bit deviation. This deviation in bits can severely degrade the test quality and overall fault coverage which may affect yield. The error resilience is the capability of the test data to resist against such bit lips. Here in this paper, the earlier methods of error resilience is compared and a Hamming code based error resilience technique is proposed to improve the error resilience capacity of compressed test data. This method is applied on Huffman code based compressed test data of widely used ISCAS benchmark circuits. The fault coverage measurement results show the effectiveness of the proposed method. The basic goal here is to survey the effect of bit lips on fault coverage and prepare a platform for further development in this avenue.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
A LOCATION-BASED RECOMMENDER SYSTEM FRAMEWORK TO IMPROVE ACCURACY IN USERBASE...ijcsa
Recommender systems are utilized to predict and recommend relevant items to system users. Item could be
in any forms such as documents, location, movie and articles. The mechanism of recommender system is
based on examination which includes users’ behaviors, item ratings, various logs (e.g. user’s history log)
and, social connections. The main objective of the examination is to predict items which have great potential to be liked by users. Although, traditional recommender systems have been very successful to predict what user might like, they did not take into consideration contextual information such as users’
location. In this paper, we propose a new framework with the aim of enhancing accuracy of recommendations in user-based collaborative filtering by considering about users’ locations.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
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.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
IMPROVING COLLABORATIVE RECOMMENDATION VIA USER-ITEM SUBGROUPSNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Improving Service Recommendation Method on Map reduce by User Preferences and...paperpublications3
Abstract: Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, to address the above challenges and presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative filtering algorithm is adopted to generate appropriate recommendations.Keywords: recommender system, user preference, keyword, Big Data, mapreduce, Hadoop.
Title: Improving Service Recommendation Method on Map reduce by User Preferences and Reviews
Author: Dayanand Bhovi, Mr. Ashwin Kumar
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
Improvement in Error Resilience in BIST using hamming codeIJMTST Journal
In the current scenario of IP core based SoC, to test the CUT we need to communication link between Circuit Under Test and ATPG, so before applying to actual DUT. If there is a problem with this link, there may be a lip in bit of test data. Compared to original test data, if there is a bit lip in the original data, the codeword may change and hence the decompressed data will have a large number of bit deviation. This deviation in bits can severely degrade the test quality and overall fault coverage which may affect yield. The error resilience is the capability of the test data to resist against such bit lips. Here in this paper, the earlier methods of error resilience is compared and a Hamming code based error resilience technique is proposed to improve the error resilience capacity of compressed test data. This method is applied on Huffman code based compressed test data of widely used ISCAS benchmark circuits. The fault coverage measurement results show the effectiveness of the proposed method. The basic goal here is to survey the effect of bit lips on fault coverage and prepare a platform for further development in this avenue.
https://www.makeyourhome.de/news-neuheiten/hier_erfahren_sie_was_ein_veganer_sessel-276
Modern. Besonders. Vegan. Das ist KUAN:Natürliche Bestandteile? Ja! Tierisch? Nein! So die Idee hinter dem Sitzmöbel. Je nach Bezugsart ist KUAN durch und durch vegan. Damit beantwortet SMV als naturverbundenes Familienunternehmen die steigende Nachfrage nach veganen und natürlichen Designmöbeln. Denn vom Kern, über die Schäume, bis hin zum ausgewählten Bezug verzichtet KUAN auf tierische Stoffe.
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.
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
Abstract—Web mining is the amalgamation of information accumulated by traditional data mining methodologies and techniques with information collected over the World Wide Web. A Recommendation system is a profound application that comforts the user in a decision-making process, where they lack of personal experience to choose an item from the confound set of alternative products or services. The key challenge in the development of recommender system is to overcome the problems like single level recommendation and static recommendation, which are exists in the real world e-services. The goal is to achieve and enhance predicting algorithm to discover the frequent items, which are feasible to be purchasable. At this point, we examine the prior buying patterns of the customers and use the knowledge thus procured, to achieve an item set, which co-ordinates with the purchasing mentality of a particular set of customers. Potential recommendation is concerned as a link structure among the items within E-commerce website, which supports the new customers to find related products in a hurry. In Existing system, a fuzzy set consists of user preference and item features alone, so the recommendations to the customers are irrelevant and anonymous. In this paper, we suggest a recommendation technique, which practices the wild spreading and data sharing competency of a huge customer linkage and also this method follows a fuzzy tree- structured model, in which fuzzy set techniques are utilized to express user preferences and purchased items are in a clustered form to develop a user convenient recommendations. Here, an incremental association rule mining is employed to find interesting relation between variables in a large database.
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
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.
Following the user’s interests in mobile context aware recommender systemsBouneffouf Djallel
The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2) how to model the user’s situation and its related interests? To the best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-ε-greedy algorithm we propose, which combines the standard ε-greedy algorithm and both content-based filtering and case-based reasoning techniques.
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
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.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
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Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
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2. 89 International Journal for Modern Trends in Science and Technology
Swapna Joshi and Prof Manisha Patil : Effective Cross-Domain Collaborative Filtering using Temporal Domain – A
Brief Survey
not like animated movies, may like to watch them
in future due to evolving superior 3D animation
technologies with very good visual effects or else if
the person having baby may like to watch it now
which he has never watched before. Another
example is a person buying outfit items of a specific
type may like to buy the outfits of different style if
he/she is relocating to an altogether different
geographical area.
Reviewers express their opinions about a particular
product or a service (or restaurants, hotels, movies,
etc.) through some review sites, social networking
sites like facebook, twitter, etc. or even over blogs.
All these act as sources for opinions.
B. Data sparsity –
In practice, many commercial recommender
systems are used to evaluate large item sets (e.g.,
Amazon.com recommends books and CDnow.com
recommends music albums). In these systems,
even active users may have purchased well under
1% of the items (1% of 2 million books is 20,000
books). Accordingly, a recommender system based
on nearest neighbor algorithms may be unable to
make any item recommendations for a particular
user. As a result the accuracy of recommendations
may be poor.
C. Scalability –
Nearest neighbor algorithms require computation
that grows with both the number of users and the
number of items. With millions of users and items,
a typical web-based recommender system running
existing algorithms will suffer serious scalability
problems.
D. Synonyms –
Synonyms refers to the tendency of a number of the
same or very similar items to have different names
or entries. Most recommender systems are unable
to discover this latent association and thus treat
these products differently. Topic Modelling (like the
Latent Dirichlet Allocation technique) could solve
this by grouping different words belonging to the
same topic.
E. Diversity and the Long Tail –
Collaborative filters are expected to increase
diversity because they help us discover new
products. Some algorithms, however, may
unintentionally do the opposite. Because
collaborative filters recommend products based on
past sales or ratings, they cannot usually
recommend products with limited historical data.
This can create a rich-get-richer effect for popular
products, akin to positive feedback. This bias
toward popularity can prevent what are otherwise
better consumer-product matches.
II. MOTIVATION
Many In most CDCF approaches more stress is
given on referring the ratings from multiple sites of
the related domains. But there is one more problem
in recommender systems that the user interests
keep drifting over a period of time. Hence the
temporal domains should also be considered in the
cross domain recommender systems. As users’
interests keep changing over time and a user is
more likely to be interested in different item
categories at different time, we cannot simply view
the multiple historical buying patterns of the same
user in different historical temporal domains.
III. RELATED WORK
The Cross domain collaborative filtering is an
evolving research topic in recommender systems.
The survey was cried to study two major issues in
current CDCF systems, data sparsity and user
interest drift over time.
[1] was studied to understand the basic
Cross-domain recommender systems. In
cross-domain, we refer multiple auxiliary domains
for user ratings/inputs from related domains and
then use these ratings to transfer the collective
rating matrix to the target domain for
recommendations. For example, to recommend a
movie of 3D animation genre to the end user, the
target domain is 3D animation genre movie. So we
consider multiple input auxiliary domains as
books, music, and other movie genre similar to the
movie subject etc. It used a derived method based
on a Bayesian latent factor model which can be
inferred using Gibbs sampling. This addresses the
challenge of modeling user-interest drift over time
by considering the historical time slice patterns of
the user.
[2] and [3] were studied for dealing with data
sparsity reduction problem. In [3], principled
matrix-based transfer learning framework is used
that takes into account the data heterogeneity.
The principle coordinates of both users and items
in the auxiliary data matrices are extracted and
transferred to the target domain in order to reduce
the effect of data sparsity.
The survey was carried out on [4], [5] and [6] to
study different methods of cross domain
3. 90 International Journal for Modern Trends in Science and Technology
Swapna Joshi and Prof Manisha Patil : Effective Cross-Domain Collaborative Filtering using Temporal Domain – A
Brief Survey
collaborative filtering with temporal domain.
In [4], the temporal domains are considered. The
ratings provided by the same user at different time
may reflect different interests as those ratings are
provided by different users. The proposed
algorithms are a variant of standard
neighborhood-based (either user based or item
based) CF. The main idea behind the proposed
approaches is to enhance inter-domains edges by
both discovering new edges and strengthening
existing ones. In [5], Factorization model and
item-item neighborhood model are used. In both
factorization and neighborhood models, the
inclusion of temporal dynamics proved very useful
in improving quality of predictions, more than
various algorithmic enhancements
While working in CDCF recommender system,
one critical aspect is how to collect and transfer the
knowledge from different input auxiliary domain to
the target domain for giving the recommendations.
The survey was done on [7], [8] and [9] to
understand the methods of transferring the
knowledge across the domain. These Transfer
learning methods are used to first individually
collect the ratings matrix for each auxiliary input
domain and then transferring the collective ratings
to the target domain.
[10] was studied rigorously which deals with the
cross domains over different sites (domains),
transferring the rating knowledge from these sites
to recommend in target domain by using
knowledge transfer method. Along with using
multiple sites/domain to collect the user ratings, it
also extends the model of [4] for using temporal
domain to deal with the issue of user interest drift
over time. It uses the principle that user has
multiple counterparts across temporal domains
and the counterparts in successive temporal
domains are different but closely related. A series
of time-slices are viewed as related CF domains and
a user at current time-slice depends on herself in
the previous time-slice. Model is built on a
cross-domain CF framework by viewing the
counterparts of the same user in successive
temporal domains are different but related users. If
we can find the unchanged rating patterns (static
components) shared across temporal domains, the
drifting factors of users (changing components) in
each temporal domain can be easily captured.
Bayesian generative model to generate and predict
ratings for multiple related CF domains on the
site-time coordinate system, is used as the basic
model for the cross-domain CF framework which is
extended for modeling user-interest drifting over
time, where a series of time-slices are viewed as
related CF domains and a user at current
time-slice depends on herself in the previous
time-slice.
Table 1 provides an overall evaluation of related
work.
Table 1: Overall Evaluation of Related work
Reference
Number
Paper Name Data
Sparsity
User
Interest
drift over
time
1
Cross-domain
recommender systems
No Yes
2
Can movies and books
collaborate? Cross
domain collaborative
filtering for sparsity
reduction
Yes No
3
Transfer learning in
collaborative filtering for
sparsity reduction
Yes No
4
Cross-domain
collaborative filtering over
time
No Yes
5
Collaborative filtering
with temporal dynamics
No Yes
6
A spatio-temporal
approach to collaborative
filtering
Yes Yes
8
Transfer learning for
collaborative filtering via
a rating-matrix generative
model
Yes No
10
Rating Knowledge
Sharing in Cross-Domain
Collaborative Filtering
Yes Yes
Table 2: Specific Evaluation of Related work (for user
interest drift over time issue)
Paper Name Using
historical time
slice data
Using
Current time
context
Cross-domain recommender
systems [1] Yes No
Cross-domain collaborative
filtering over time [4] Yes No
Collaborative filtering with
temporal dynamics [5] Yes No
A spatio-temporal approach to
collaborative filtering [6] Yes No
Rating Knowledge Sharing in
Cross-Domain Collaborative
Filtering [10] Yes No
Proposed System Yes Yes
IV. PROPOSED WORK
After the rigorous survey, the issue specific to
user interest drift over time needs to be handled
effectively using current time context. As per the
survey of existing work [10], the user interest drift
over time problem in Cross domain collaborative
filtering techniques is handled by introducing the
concept of temporal domain. The historical rating
data is time stamped. With the input historic data
4. 91 International Journal for Modern Trends in Science and Technology
Swapna Joshi and Prof Manisha Patil : Effective Cross-Domain Collaborative Filtering using Temporal Domain – A
Brief Survey
from the same or different user having similar
interests of the past buying patterns, the data set is
divided in multiple time slices. Each time slice is
considered as one time domain and then the
principles of cross domain filtering are applied on
these multiple time domains. The knowledge is
transferred along successive temporal domains to
benefit the user-counterparts in each domain. A
user in different temporal domains can be treated
as a set of user-counterparts with similar but
different interests. Thus, the ratings provided by
the same user at different time may reflect different
interests as those ratings are provided by different
users. In this current work, only the historical time
domain is being considered by gathering the
historical ratings provided by same or different
user having same taste.
The proposed work take into account the current
time domain context along with historical data.
This can be viewed in multiple contexts/aspects.
Contexts can be added based on the season
changes, location changes, cultural changes,
festival periods ect. By using these current
contextual parameters along with the existing work
of historical time domain, the user
recommendations will be more effective and more
current addressing the current context of the user.
The dataset pre-processing will divide the input
dataset of each year into 12 different slices having
one slice per month as opposed to the 4 slices per
year in the base system. By this more granular
level rating matrix can be generated which is more
aligned with the ever changing user interests. This
can provide better ratings recommendations. Also
the contextual parameters will be applied on these
slices on granular levels so that the season,
holidays, locations will be applied more closely to
the current matching context.
The block diagram in fig. 2 shows the proposed
work. First the dataset will be initialized by
acquiring the data from various sites. Dataset
pre-processing will create the user * movies matrix
in 12 different time slices.
Then the average rating will be calculated by
applying the current contextual parameters and
comparing the historical ratings as per the time
slices. Ten the final ratings recommendations will
be presented to the user.
Fig. 2: Proposed work
V. CONCLUSION
We have given a brief survey of cross domain CF
over Temporal domain and summarized the related
works for different models dealing with the issues
mentioned above. Studied papers are using only
the previous history of either same user or user
groups for recommendation of current time slice.
As a part of future work, we can consider the
current time slice parameters as well depending on
different time aware contexts such as festivals,
seasons, regional and cultural changes etc. as per
current user’s geographic locations. This will
enhance the recommendations more in line with
the current time domain.
ACKNOWLEDGEMENT
This survey would not have been possible
without the support and help of many individuals. I
would like to extent my sincere thanks to all of
them. I am indebted to Prof. Manisha Patil,
Department of Computer Engineering of Smt.
Kashibai Navale College of Engineering affiliated to
Savitribai Phule Pune University for her guidance
and constant supervision as well as all the other
staff members for providing important information
regarding the survey.
REFERENCES
[1] P. Cremonesi, A. Tripodi, and R. Turrin,
“Cross-domain recommender systems,” in Proc.
IEEE 11th Int. Conf. Data Mining Workshops
(ICDMW), Vancouver, BC, Canada, 2011, pp.
496–503.
[2] B. Li, Q. Yang, and X. Xue, “Can movies and books
collaborate? Cross domain collaborative filtering for
sparsity reduction,” in Proc. 21st Int. Joint Conf.
Artif. Intell. (IJCAI), 2009, pp. 2052–2057.
[3] W. Pan, E. W. Xiang, N. N. Liu, and Q. Yang,
“Transfer learning in collaborative filtering for
sparsity reduction,” in Proc. 24th Conf. Artif.Intell.
5. 92 International Journal for Modern Trends in Science and Technology
Swapna Joshi and Prof Manisha Patil : Effective Cross-Domain Collaborative Filtering using Temporal Domain – A
Brief Survey
(AAAI), 2010, pp. 230–235.
[4] B. Li et al., “Cross-domain collaborative filtering over
time,” in Proc. 22nd Int. Joint Conf. Artif. Intell.
(IJCAI), 2011, pp. 2293–2298.
[5] Y. Koren, “Collaborative filtering with temporal
dynamics,” in Proc. Int. Conf. Knowl. Discov. Data
Mining (KDD), Paris, France, 2009, pp. 447–456.
[6] Z. Lu, D. Agarwal, and I. S. Dhillon, “A
spatio-temporal approach to collaborative filtering,”
in Proc. 3rd ACM Conf. Recommender Syst., New
York, NY, USA, Oct. 2009, pp. 13–20.
[7] R. Salakhutdinov and A. Mnih, “Bayesian
probabilistic matrix factorization using Markov
chain Monte Carlo,” in Proc. 25th Int. Conf. Mach.
Learn. (ICML), Helsinki, Finland, 2008, pp. 880–887.
[8] B. Li, Q. Yang, and X. Xue, “Transfer learning for
collaborative filtering via a rating-matrix generative
model,” in Proc. 26th Annu. Int. Conf. Mach. Learn.
(ICML), 2009, pp. 617–624.
[9] S. J. Pan and Q. Yang, “A survey on transfer
learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no.
10, pp. 1345–1359, Oct. 2010.
[10] Bin Li, Xingquan Zhu, Ruijiang Li, and Chengqi
Zhang “Rating Knowledge Sharing in Cross-Domain
Collaborative Filtering” in IEEE TRANSACTIONS ON
CYBERNETICS, VOL. 45, NO. 5, MAY 2015
[11] B. Li, “Cross-domain collaborative filtering: A brief
survey,” in Proc. 23rd IEEE Int. Conf. Tools Artif.
Intell. (ICTAI), Boca Raton, FL, USA, Nov. 2011, pp.
1085–1086.