Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Introduction: personalized views of personalization
Communications of the ACM
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups
Revised Papers from the nternational Workshops OHS-7, SC-3, and AH-3 on Hypermedia: Openness, Structural Awareness, and Adaptivity
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Experiments in dynamic critiquing
Proceedings of the 10th international conference on Intelligent user interfaces
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Accounting for taste: using profile similarity to improve recommender systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Collaborative filtering for predicting users' potential preferences
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
The relation between user intervention and user satisfaction for information recommendation
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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Many recommender systems employed in commercial web sites use collaborative filtering. The main goal of traditional collaborative filtering techniques is improvement of the accuracy of recommendation. Nevertheless, such techniques present the problem that they include many items that the user already knows. These recommendations appear to be good when we consider accuracy alone. On the other hand, when we consider users' satisfaction, they are not necessarily good because of the lack of discovery. In our work, we infer items that a user does not know by calculating the similarity of users or items based on information about what items users already know. We seek to recommend items that the user would probably like and does not know by combining the above method and the most popular method of collaborative filtering.