A topic sentiment based method for friend recommendation in online social networks via matrix factorization

Data sparsity and prediction quality have been recognized as the crucial challenges in recommender system. With the expansion of social network data, social network analysis is becoming more and more important. Traditional Recommendation System assumes that users are independent and distributed equally, which ignores the social interaction or connection among users. In order to solve the prediction quality of friend recommendation in social networks, a user recommendation algorithm for social networks based on sentiment analysis and matrix factorization is proposed in this paper. This method is based on the traditional matrix factorization model. By integrating Sentiment (S), Important (I) and Objective (O) of user topic content in the social network, this paper proposes the approach base on sentiment analysis and matrix factorization to solve the poor prediction accuracy by employing social network. SIO model solves the problem that users in social networks can't score the content of topics. Usertopic matrix is constructed by SIO model. Combining the SIO model with matrix factorization, algorithm called SIO-TMF algorithm is proposed. Applying this method on social network, comparing with some traditional recommendation algorithms from four aspects: accuracy, diversity, novelty and coverage, the experimental results show that the proposed method improves the prediction quality of recommender system. (C) 2019 Published by Elsevier Inc.