IJMTES – MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS USING CONTEXTUAL OPERATING TENSOR

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

Paper Title : MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS USING CONTEXTUAL OPERATING TENSOR

Author’s Name : Dr Rajagopalan | R Sudha | N Sharmilaunnamed

Volume 04 Issue 03 2017

ISSN no:  2348-3121

Page no: 70-72

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 are 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. The existing approach is dealt with Context Operating Tensor (COT) model yields significant improvements over the competitive compared methods on three typical data sets

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