A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
A novel collaborative filtering-based framework for personalized services in m-commerce
Proceedings of the 16th international conference on World Wide Web
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Applying relevant set correlation clustering to multi-criteria recommender systems
Proceedings of the third ACM conference on Recommender systems
A context-aware recommender system for M-commerce applications
AMT'11 Proceedings of the 7th international conference on Active media technology
Accuracy improvements for multi-criteria recommender systems
Proceedings of the 13th ACM Conference on Electronic Commerce
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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With the rapid growth of wireless technologies and mobile devices, there is a great demand for personalized services in m-commerce. Collaborative filtering (CF) is one of successful techniques to produce personalized recommendations for users. This paper proposes a novel approach to improve CF algorithms, where the contextual information of a user and the multicriteria ratings of an item are considered besides the typical information on users and items. The multilinear singular value decomposition (MSVD) technique is utilized to explore both explicit relations and implicit relations among user, item and criterion. We implement the approach in an existing m-commerce platform, and encouraging experimental results demonstrate its effectiveness.