Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation

2019/03/19 by Yong Liu, Liu, Yong, Yinan Zhang +13 · 3 citations
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Information Retrieval (cs.IR) #Stochastic Gradient Optimization Techniques #Tensor decomposition and applications

paper · pdf · doi:10.48550/arxiv.1903.07826

openalex publication_date 2019/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years. Most previous interactive recommendation systems only focus on optimizing recommendation accuracy while overlooking other important aspects of recommendation quality, such as the diversity of recommendation results. In this paper, we propose a novel recommendation model, named \underlineDiversity-promoting \underlineDeep \underlineReinforcement \underlineLearning (D²RL), which encourages the diversity of recommendation results in interaction recommendations. More specifically, we adopt a Determinantal Point Process (DPP) model to generate diverse, while relevant item recommendations. A personalized DPP kernel matrix is maintained for each user, which is constructed from two parts: a fixed similarity matrix capturing item-item similarity, and the relevance of items dynamically learnt through an actor-critic reinforcement learning framework. We performed extensive offline experiments as well as simulated online experiments with real world datasets to demonstrate the effectiveness of the proposed model.

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• Leveraging Media Frames to Improve Normative Diversity in News Recommendations
• Large Language Model-Enhanced Reinforcement Learning for Diverse and Novel Recommendations
• Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design

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