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
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
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
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
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Hi-index | 0.00 |
The purpose of this paper is to examine how singular value decomposition (SVD) and demographic information can improve the performance of plain collaborative filtering (CF) algorithms. After a brief introduction to SVD, where the method is explained and some of its applications in recommender systems are detailed, we focus on the proposed technique. Our approach applies SVD in different stages of an algorithm, which can be described as CF enhanced by demographic data. The results of a rather long series of experiments, where the proposed algorithm is successfully blended with user-and item-based CF, show that the combined utilization of SVD with demographic data is promising, since it does not only tackle some of the recorded problems of recommender systems, but also assists in increasing the accuracy of systems employing it.