Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

TL;DR

This paper introduces DRR, a deep reinforcement learning framework for recommendation systems that models dynamic user-item interactions and optimizes long-term rewards, outperforming existing methods.

Contribution

The paper proposes a novel DRR framework using Actor-Critic reinforcement learning to explicitly model user-item interactions and long-term rewards in recommendations.

Findings

DRR outperforms state-of-the-art methods in experiments.

Explicit interaction modeling improves recommendation quality.

Long-term reward optimization enhances user satisfaction.

Abstract

Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies suffer from two limitations: (1) considering the recommendation as a static procedure and ignoring the dynamic interactive nature between users and the recommender systems, (2) focusing on the immediate feedback of recommended items and neglecting the long-term rewards. To address the two limitations, in this paper we propose a novel recommendation framework based on deep reinforcement learning, called DRR. The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards. Furthermore, a state representation module is incorporated into DRR, which can explicitly capture the interactions between items and users. Three instantiation structures are developed. Extensive experiments on four real-world datasets are conducted under both the offline and online evaluation settings. The experimental results demonstrate the proposed DRR method indeed outperforms the state-of-the-art competitors.