Deep Reinforcement Learning Based Group Recommendation System with Multi-Head Attention Mechanism
Published In
2023 Fifth International Conference on Transdisciplinary AI (transai)
Document Type
Citation
Publication Date
2023
Abstract
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.
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DOI
10.1109/TransAI60598.2023.00038
Persistent Identifier
https://archives.pdx.edu/ds/psu/41278
Publisher
IEEE
Citation Details
Izadkhah, S., & Reakbdar, B. (2023, September 25). Deep Reinforcement Learning Based Group Recommendation System with Multi-Head Attention Mechanism. 2023 Fifth International Conference on Transdisciplinary AI (TransAI). https://doi.org/10.1109/transai60598.2023.00038