Tensor factorization method based on review text semantic similarity for rating prediction

Abstract

Recommendation methods have been proved to be successful in eliminating the information overload problem. However, they are still insufficient as far as data sparsity and cold start issues are concerned. Some approaches have attempted to resolve these issues by utilizing relevant information contained in user review text, although, the type of information extracted from the review text and the way such recommendation methods utilize the information affect recommendation accuracy of the models. In this paper, we address such challenges by considering linguistic similarity between review texts and employ it as additional factor in rating prediction, and we propose a recommendation method based on tensor factorization which involves review text semantic similarity. The proposed tensor factorization model supplements the central task of factorization methods of finding similar users, uncovering underlying characteristics of the data and predicting user preferences by introducing text semantic similarity. The proposed method is carried out in two main phases; first phase, by computing semantic similarity between review texts and assigning a similarity score, and second phase by introducing the similarity score as an additional factor in the probabilistic matrix factorization (PMF) model. To evaluate the performance of the proposed approach, several Amazon datasets were experimented and the results verify that the semantic similarity of review texts not only successfully portray user preferences, and extend PMF to include review texts similarities but also increases prediction influence which results in improved performance.