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
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Machine Learning
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
IEEE Transactions on Knowledge and Data Engineering
Personalized mining of web documents using link structures and fuzzy concept networks
Applied Soft Computing
A hybrid approach for movie recommendation
Multimedia Tools and Applications
A comparative user study on rating vs. personality quiz based preference elicitation methods
Proceedings of the 14th international conference on Intelligent user interfaces
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
NEWER: A system for NEuro-fuzzy WEb Recommendation
Applied Soft Computing
Improving Recommender Systems by Incorporating Social Contextual Information
ACM Transactions on Information Systems (TOIS)
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Recommendation techniques are utilized in electronic commerce because of their potential commercial value. Many e-commerce sites employ collaborative filtering techniques to provide recommendations to customers based on the preferences of similar users. However, as the number of customers and the range of products increase, the prediction accuracy of memory-based collaborative filtering algorithms declines because of sparse ratings. In addition, the time complexity of such algorithms is quite high in the prediction phase. To resolve these issues, we propose a genre-based fuzzy inference filtering approach for predicting movie preferences. We use content-based and collaborative filtering algorithms as baseline methods to evaluate the performance of our approach. The results of experiments demonstrate that the hybrid approach exploits the strengths of the content-based and collaborative filtering algorithms to achieve effective filtering in terms of precision. Moreover, the computation time can be reduced by using the α-cut approach. The findings have implications for the design of an interactive movie recommendation system for the World Wide Web.