Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Analytics-driven solutions for customer targeting and sales-force allocation
IBM Systems Journal
Improved neighborhood-based algorithms for large-scale recommender systems
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Proceedings of the fourth ACM conference on Recommender systems
Recommender Systems Handbook
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Beyond rating prediction accuracy: on new perspectives in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Beyond rating prediction accuracy: on new perspectives in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM international conference on Web search and data mining
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This paper proposes a novel method for estimating unknown ratings and recommendation opportunities and illustrates the practical implementation of the proposed approach by presenting a certain variation of the classical k-NN method in neighborhood-based collaborative filtering systems using weighted percentiles. We conduct an empirical study showing that the proposed method outperforms the standard user-based collaborative filtering approach by a wide margin in terms of item prediction accuracy and utility-based ranking metrics across various experimental settings. We also demonstrate that this performance improvement is not achieved at the expense of other popular performance measures, such as catalog coverage and aggregate diversity. The proposed approach can also be applied to other popular methods for rating estimation.