IJMTES – CONSUMER PREFERENCES FOR RECOMMENDER SYSTEM USING CONTEXT OPERATING TENSOR

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : CONSUMER PREFERENCES FOR RECOMMENDER SYSTEM USING CONTEXT OPERATING TENSOR

Author’s Name : Swetha R K | Deepika S | Pavithra K | Dr P Selvi Rajendranunnamed

Volume 04 Issue 04 2017

ISSN no :  2348-3121

Page no: 118-122

Abstract – The rapid growth of various applications on the Internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Contextual information is a significant factor in modeling the user behavior, various context-aware recommendation methods have been proposed recently. The state-of-the-art context modeling methods usually treat contexts as certain dimensions similar to those of users and items, and capture relevancies between contexts and users/items. Such kind of relevance information has some difficulty and not intuitive to the user. It is not useful properly because multi-domain relation prediction can also be used for the context aware recommendation; there is some limitations over there. Adding some contextual information with the semantics such as user-item interaction, the contextual operation will be modeled by multiplying the operating tensor with latent vectors of contexts. It is dealt with Context Operating Tensor (COT) model yields significant improvements over the competitive compared methods on three typical data sets like companion, time and location.

Key Words – Recommender system, COT, Context Operating Tensor, Video Retrieval

References

  1. S. Roy, M. Conti, S. Setia, and S. Jajodia, ―Secure Data Aggregation in Matrix factorization,‖ IEEE Trans. Information Forensics and Security, vol. 7, no. 3, pp. 1040-1052, June 2012.
  2. Collaborative Tensor Factorization and its Application in POI Recommendation , S. Rendle, Z. Gantner, C. Freudenthaler, L.
    Schmidt- Thieme,2011
  3. Modeling User Activity Preference by Lever aging User Spatial Temporal Characteristics in LBSNs,A. Karatzoglou,
  4. X. Amatriain, L. Baltrunas, N. Oliver,2010 [4] Matchbox: Large Scale Online Bayesian Recommendations, A.Karatzoglou,X. Amatriain, L. Baltrunas, N. Oliver,2010
  5. Like like alike Joint Friendship and Interest Propagation in Social Networks, S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, H. Zha,2009
  6. L. R. Tucker, “Some mathematical notes on three-mode factor analysis”, Psychometrika, vol. 31, no. 3, pp. 279-311, 1966.
  7. G. Adomavicius and A.Tuzhilin, Context-aware recommender systems,in Recommender Systems Handbook. Springer,2011, pp. 217–253.
  8. S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmid Thieme, ―Fast context-aware recommendations with factorization machines,‖ in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2011, pp.635–644.
  9. D. H. Stern, R. Herbrich, and T. Graepel, ―Matchbox: Large scale online Bayesian recommendations,‖ in Proceedings of the 18th International Conference on World Wide Web. , 2009, pp111–120.