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

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…