Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
This addresses the need for more balanced recommendation quality beyond accuracy for users, though it is incremental as it builds on existing interactive recommendation methods.
The paper tackles the problem of interactive recommendation systems overlooking diversity by proposing D^2RL, a model that uses Determinantal Point Processes and reinforcement learning to generate diverse and relevant recommendations, achieving improved diversity metrics in offline and simulated online experiments.
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 \underline{D}iversity-promoting \underline{D}eep \underline{R}einforcement \underline{L}earning (D$^2$RL), 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.