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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
Scalable Tensor Decompositions for Multi-aspect Data Mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Item Recommendation in Collaborative Tagging Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In the web world, there are massive data-items that are readily available, and it has become a tedious job to identify needed items for users. Therefore, there is a need of recommender system that analyzes the user's behavior and accordingly recommend the items. Existing algorithm uses cubic analysis approach to grab three-way correlations like user-item-tag or user-item-rating. This analysis totally relies on ratings provided by users, but it does not use at-all user-item-tag profile for the analysis, hence does not take full advantage of user's transaction history. This paper proposes an approach to incorporate profile (meta-data) information of user-tag-item for analysis along with cubic approach. This lead to a proper understanding of user, and improving the quality of recommendations. Moreover, the major issue in the recommendation is sparsity, i.e., most of the entries in the tensor remain zero, so it is worth to store only non-zero values. Hence, we use co-ordinate format approach to store and access data into a tensor. The solution has been evaluated by applying MovieLens data set in the proposed approach, and the result is caused by computing precision against top-N recommended items. Since, the proposed approach has incorporated meta-data information, the result generated is better than the existing solution.