Journal Title : International Journal of Modern Trends in Engineering and Science
Paper Title : CONSUMER PREFERENCES FOR RECOMMENDER SYSTEM USING CONTEXT OPERATING TENSOR
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
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