Efficient Deep Reinforcement Learning-Enabled Recommendation
IEEE Transactions on Network Science and Engineering(2023)
Abstract
Existing recommendations based on machine learning are mainly based on supervised learning. However, these methods affected by historical behavior often bring great difficulties on mining high-quality long-tail items, achieving cold-start recommendations, and causing response inability to real-time environment changes. To this end, this paper proposes a Deep Reinforcement Learning-enabled Recommendation based on Hierarchical attention and Sample-enhanced priority experience replay (HEDRL-Rec). First, we propose a hierarchical attention mechanism to extract more hidden information, including different contributions from single feature and overall feature (comprising combined feature), for enhancing features extraction ability of Actor-Critic architecture. Then, by considering the reusability of historical experiences and differences their contributions, we then propose a sample-enhanced priority experience replay mechanism to alleviate the problems of sample imbalance, sparse data, and excessive action space, where, thereby realizing personalized recommendations in real-time changing environments. Finally, we develop a deep reinforcement learning-enabled recommendation algorithm to solve the problems of non-convergence in the Critic. Extensive experiments demonstrate that, in particular, the recommended Click-Through Rate (CTR) of the HEDRL-Rec is 10.55% higher than the state-of-the-art LIst-wise Recommendation framework based on the Deep Reinforcement learning (ILRD) scheme, while the HEDRL-Rec has better stability and usability in the recommendation scenario, effectively alleviating the cold-start problem of systems lacking manual annotation data.
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Key words
Recommendation,Deep reinforcement learning,Attention mechanism,Experience replay mechanism,Unsupervised learning
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