A FILTERING BUBBLE TECHNIQUE FOR A RECOMMENDER SYSTEM USING COT MODEL

Computer Enginnering

With the rapid growth of information on the internet, recommender systems become fundamental for helping users alleviate the problem of information overload.Since contextual information can be used as a significant factor in modeling user behavior, context modeling various context-aware recommendation methods are proposed. However, the context modeling methods treat contexts as other dimensions similar to the dimensions of users and items, and cannot capture the special semantic operation of contexts.On the other hand, some works on multi-domain relation prediction can be used for the context-aware recommendation, but they have problems in generating recommendation under a large amount of contextual information.In this work, pro-pose Contextual Operating Tensor(COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector. Then, to model the semantic operation of a context combination, we generate a contextual operating matrix from the contextual operating tensor and latent vectors of Contexts. Thus latent vectors of users and items can be operated by the contextual operating matrices. Experimental results show that the proposed COTmodelyields significant improvements over the competitive compared methods on three typical datasets,i.e., Food, MdatasetsAccommodation and MovM data sets.

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