Negative Feedback Aware Hybrid Sequential Neural Recommendation Model

Content-based (CB) and collaborative filtering (CF) are two classical types of recommendation methods that widely applied in various online services. Recently, sequential based recommender systems achieved good performance. However, how to integrate the advantages of these recommendation systems has not been well studied yet. Besides, most previous algorithms conduct negative sampling for each user based on items the user has not interacted with for model training, while it is unreasonable when there is known users’ negative feedback over items. We believe that a user’s negative feedback is valuable and should be used to better model users’ preferences. In this study, we propose a novel negative feedback aware hybrid sequential recommendation model (NFHS) to take the advantages of these three types of recommendation systems and to directly utilize negative feedback. There are two modules in our algorithm: 1) a static module to model the interaction history and the content features of the user and the current item. 2) a sequence module to distill a user’s interaction sequence features, negative feedback has also been directly introduced into this module. The experimental results on two real-world datasets from distinct scenarios demonstrate our model significantly outperforms various state-of-the-art approaches.