GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
List-wise learning to rank with matrix factorization for collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
A mechanism that provides incentives for truthful feedback in peer-to-peer systems
Electronic Commerce Research
Collaborative error-reflected models for cold-start recommender systems
Decision Support Systems
Improving the performance of recommender system by exploiting the categories of products
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Hi-index | 0.00 |
User-based collaborative filtering methods typically predict a user's item ratings as a weighted average of the ratings given by similar users, where the weight is proportional to the user similarity. Therefore, the accuracy of user similarity is the key to the success of the recommendation, both for selecting neighborhoods and computing predictions. However, the computed similarities between users are somewhat inaccurate due to data sparsity. For a given user, the set of neighbors selected for predicting ratings on different items typically exhibit overlap. Thus, error terms contributing to rating predictions will tend to be shared, leading to correlation of the prediction errors. Through a set of case studies, we discovered that for a given user, the prediction errors on different items are correlated to the similarities of the corresponding items, and to the degree to which they share common neighbors. We propose a framework to improve prediction accuracy based on these statistical prediction errors. Two different strategies to estimate the prediction error on a desired item are proposed. Our experiments show that these approaches improve the prediction accuracy of standard user based methods significantly, and they outperform other state-of-the-art methods.