An empirical study on hybrid recommender system with implicit feedback

Abstract

The amount of data generated by systems is growing quickly because of the appearance of mobile devices, wearable devices, and The Internet of Things (IoT), to name a few. Because of that, the importance of personalized recommendations by recommender systems becomes more important for consumers inundated with vast amount of choices. Many different types of data are generated implicitly (for example, purchase history, browsing activity, and booking history), and less intrusive recommendation systems can be built upon implicit feedback. There are previous efforts to build a recommender system with implicit feedback by estimating the latent factors or learning the personalized ranking but these approaches do not fully take advantage of various types of information that can be created from implicit feedback such as implicit profiles or a popularity of items. In this paper, we propose a hybrid recommender system which exploits implicit feedback and demonstrate better performance of the proposed recommender system based on the expected percentile ranking and a precision-recall curve against two state-of-the-art recommender systems, Bayesian Personalized Ranking (BPR) and Implicit Matrix Factorization methods, using hotel reservation data.