Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
This work addresses the challenge of leveraging negative feedback in recommender systems for e-commerce users, representing an incremental improvement by integrating both feedback types into a reinforcement learning approach.
The paper tackles the problem of incorporating both positive and negative user feedback in recommender systems, which is challenging due to the imbalance where negative feedback can overwhelm positive signals, and proposes a deep reinforcement learning framework (DEERS) that models interactions as a Markov Decision Process, with experimental results on real-world e-commerce data demonstrating its effectiveness.
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedback. Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations. However, the number of negative feedback is much larger than that of positive one; thus incorporating them simultaneously is challenging since positive feedback could be buried by negative one. In this paper, we develop a novel approach to incorporate them into the proposed deep recommender system (DEERS) framework. The experimental results based on real-world e-commerce data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of both positive and negative feedback in recommendations.