An Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods

One of the main challenges in recommender systems is data sparsity, which leads to high variance. Several attempts have been made to improve the bias-variance trade-off using auxiliary information. In particular, document modeling-based methods have improved the model's accuracy by using textual data such as reviews, abstracts, and storylines when the user-to-item rating matrix is sparse. However, such models are insufficient to learn optimal representation for users and items. For building recommender systems, user-based and item-based collaborative filtering have long been used due to their efficiency. A user and item profile are created based on their historically interacted items and the users who interacted with the target item. In spite of the fact that these two approaches have been studied separately, there has been little research into combining them. The purpose of this study is to combine these two approaches by considering the opinions of users on these items. Each user is represented by their historical behavior, while each item is represented by the users who have interacted with it before, combined with contextual information, which is processed with NLP. The proposed algorithm is implemented and tested on three real-world datasets that demonstrate our model's effectiveness over the baseline methods.