Neural text similarity of user reviews for improving collaborative filtering recommender systems

According to the advent of technology and the expansion of using the World Wide Web, there has been an enormous increase in the number of Internet retailers and their customers. The amount of data is exploding due to the numerous users and the variety of products. In such a condition, recommender systems play an important role in helping users and providing suggestions based on their taste. Using recommender systems’ suggestions, users can save their time on finding their personalized, useful, and favorite items without being overwhelmed by a large set of items. User-based collaborative filtering recommender systems for each user find similar users based on their ratings and suggest their favorite products to the intended user. In this paper, we present a model to improve recommender systems by finding similar users based on their reviews in addition to their ratings. To this end, we compute users’ reviews similarity utilizing seven different approaches, out of which two techniques are lexical-based, two techniques benefit from the neural representation of words, and three techniques are based on the neural representation of texts. Our experiments on two different datasets from Amazon show that the proposed model for capturing review’s similarity significantly improved the performance of the recommender system. Moreover, the model based on Long Short Term Memory (LSTM) network achieves the best results.