Modeling Side Information in Preference Relation based Restricted Boltzmann Machine for recommender systems

A majority of the collaborative filtering techniques exploit user-item rating information to generate recommendations of unseen items for a user. However, a user's preference also depends on some extra information like item features, user attributes and others, which is known as side information. Further, according to recent studies, using preference relation as an alternative to absolute ratings often produces quality recommendations. This study proposes a collaborative filtering technique using Preference Relation based Restricted Boltzmann Machine for recommender system. The proposed method takes the preference relations of items as input and generates a ranking of items for any user. Using Conditional Restricted Boltzmann Machine, the side information of items along with preference relations are integrated into the model. Besides side information, the proposed method is also able to capture second order and higher order user-item interactions. Experimental verification of the proposed model is done using three datasets: MovieLens-1M, MovieLens-20M, and Book-Crossing, which are the most widely used datasets for testing recommender systems. Results obtained at different positions using standard ranking measures like, NDCG and MAP, indicate that the performance of the proposed method is better compared to related state-of-the-art collaborative filtering models for Top-N recommendation task. (C) 2019 Elsevier Inc. All rights reserved.