An Item–Item Collaborative Filtering Recommender System Based on Item Reviews: An Approach with Deep Learning

Abstract

Item−item collaborative filtering is a sub-type of a recommender system that applies the items’ similarities for recommending a new set of items to the user. Usually, a traditional recommender system utilizes items’ ratings given by the user for deducing their preferences for recommending items. However, for the popularity of social platforms, users are now more familiar to write textual comments known as reviews about items based on their experiences rather than giving a rating, because rating any item limits a user to manifest the degree of satisfaction towards the item. As a result, the items’ reviews become a precious source of information that could enhance the system’s performance. In this paper, a novel recommendation approach has been proposed by applying a recurrent neural network to incorporate items’ reviews with the recommender system. The recurrent neural network is a deep learning-based approach that can distribute the text to the relevant classes. Thus, the proposed approach has applied long short-term memory which is a modern formation of recurrent neural network that is applied to compute items’ rating scores from the items’ reviews. Then, the score is used to define the uniformity of items by using the Jaccard and Pearson correlation coefficient. The proposed approach has been evaluated by two familiar datasets named Yelp & Amazon datasets. Also, it is found that the proposed approach surpasses the traditional techniques and also improved the accuracy of prediction for the Yelp dataset by approximately in respect of 1.37% mean absolute error, 2.17% precision, 2.08% recall, and 2.11% f-measure. Furthermore, the proposed approach increased the recommendation performance for Amazon dataset on average in term of 1.34% mean absolute error, 2.09% precision, 2.53% recall, and 2.32% f-measure, respectively.