Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework for recommender systems that effectively incorporates both positive and negative user feedback to adapt strategies over time, improving recommendation quality.

Contribution

It presents a novel deep reinforcement learning approach that models user interactions as an MDP and effectively integrates negative feedback, which is often overlooked.

Findings

The proposed method outperforms traditional recommenders on real-world e-commerce data.

Incorporating negative feedback significantly enhances recommendation accuracy.

Both positive and negative feedback are crucial for optimal recommendation strategies.

Abstract

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.