Deep Reinforcement Learning Based Group Recommendation System with Multi-Head Attention Mechanism

The role of group recommender systems is pivotal in recommending content to groups of users across various information systems. In this paper, we propose, a deep reinforcement learning based Group Recommendation system with Multi-head Attention mechanism (GRMA), to address the need for dynamic preference aggregation in group recommender systems. By incorporating multi-head attention, GRMA captures varying weights of group members and their interactions with different items, resulting in a comprehensive modeling of member-item interactions. The multi-head attention mechanism enhances the system's adaptability to varying group dynamics and preferences, enabling personalized and context-aware recommendations. Evaluations on the MovieLens-Rand dataset, a random group dataset generated from MovieLens 1M, using two baselines (DRGR and AGREE), demonstrate that GRMA outperforms DRGR by enabling a more comprehensive modeling of member-item interactions. Moreover, GRMA achieves a better NDCG@K score compared to AGREE as GRMA using a multi-head attention mechanism improves ranking quality by placing more pertinent items at the top of the recommendation list.