GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Enhanced prediction algorithm for item-based collaborative filtering recommendation
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
Using a trust network to improve top-N recommendation
Proceedings of the third ACM conference on Recommender systems
Connecting with the collective: self-contained reranking for collaborative recommendation
Proceedings of the 1st ACM international workshop on Connected multimedia
Collaborative error-reflected models for cold-start recommender systems
Decision Support Systems
βP: A novel approach to filter out malicious rating profiles from recommender systems
Decision Support Systems
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Collaborative Filtering recommender system, one of the most representative systems for personalized recommendations in E-commerce, is a system assisting users in easily finding useful information. However, traditional collaborative filtering systems are typically unable to make good quality recommendations in the situation where users have presented few opinions; this is known as the cold start problem. In addition, the existing systems suffer some weaknesses with regard to quality evaluation: the sparsity of the data and scalability problem. To address these issues, we present a novel approach to provide enhanced recommendation quality supporting incremental updating of a model through the use of explicit user feedback. A model-based approach is employed to overcome the sparsity and scalability problems. The proposed approach first identifies errors of prior predictions and subsequently constructs a model, namely the user-item error matrix, for recommendations. An experimental evaluation on MovieLens datasets shows that the proposed method offers significant advantages both in terms of improving the recommendation quality and in dealing with cold start users.