GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Introduction to recommender systems: Algorithms and Evaluation
ACM Transactions on Information Systems (TOIS)
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Improving personalized services in mobile commerce by a novel multicriteria rating approach
Proceedings of the 17th international conference on World Wide Web
CMAP: effective fusion of quality and relevance for multi-criteria recommendation
Proceedings of the fourth ACM international conference on Web search and data mining
Multi-criteria service recommendation based on user criteria preferences
Proceedings of the fifth 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
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In the next generation of recommender systems, multi- criteria recommendation could be regarded as one of the most important branches. Compared with traditional recommender systems with usually one single rating, multi-criteria recommender systems have several ratings from different aspects, and generally describe users' interests more accurately. However, owing to the cost of ratings, multi-criteria recommender systems meet more severe data sparsity problem than traditional single criteria recommender systems. In this paper, We design a new approach to compute the similarity between users, which tackles the challenge posed by data sparsity that one cannot obtain the similarity between users with no common rated items. With a new method of data preprocessing, the features of items are combined to eliminate the effect of noise and evaluation scale. We model the aggregation function using support vector regression which is more accurate and robust than linear regression. The experiments demonstrate that our method produces a better performance, while providing more powerful suitability on sparse and noisy datasets.