Bayesian recommender system for social information sharing: Incorporating tag-based personalized interest and social relationships

Abstract

Personal information management enables users to manage and classify information via the social tagging. The personal information management platform has recently successfully adopted social networks, enabling users to conveniently share their preferences of information with each other. The emerging social networks generate new concepts for designing modern recommender systems in personal information management and sharing platforms.

To design a recommender mechanism for the personal information management and sharing platforms, this work incorporates tag-based personalized interest and social network relationships into a modified Bayesian probability model.

The proposed system is demonstrated with experimental datasets obtained from a popular social resource sharing website. The performances of the proposed system are evaluated based on the word2vec word embedding model. Experimental results indicate that incorporating social network information and personalized tag-based preference with the Bayesian model can improve the recommendation quality for social information sharing websites.

Keywords

Knowledge filtering recommender systems social networks folksonomy social tagging personal information management Bayesian classifier

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