期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 34, 期 5, 页码 2443-2457出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2020.3012346
关键词
Social network services; Learning (artificial intelligence); Recommender systems; Machine learning; Task analysis; Estimation; Standards; DQN; reinforcement learning; recommender systems; social networks
类别
资金
- National Natural Science Foundation of China [61672445]
- Hong Kong Polytechnic University [G-YBP6]
This paper proposes a method to address the issues of data sparsity and cold-start in recommender systems by leveraging social networks. Two algorithms based on this method are developed and the experimental results show their outstanding performance on real-world datasets with reasonable computation cost.
Recommender systems aim to accurately and actively provide users with potentially interesting items (products, information or services). Deep reinforcement learning has been successfully applied to recommender systems, but still heavily suffer from data sparsity and cold-start in real-world tasks. In this work, we propose an effective way to address such issues by leveraging the pervasive social networks among users in the estimation of action-values (Q). Specifically, we develop a Social Attentive Deep Q-network (SADQN) to approximate the optimal action-value function based on the preferences of both individual users and social neighbors, by successfully utilizing a social attention layer to model the influence between them. Further, we propose an enhanced variant of SADQN, termed SADQN++, to model the complicated and diverse trade-offs between personal preferences and social influence for all involved users, making the agent more powerful and flexible in learning the optimal policies. The experimental results on real-world datasets demonstrate that the proposed SADQNs remarkably outperform the state-of-the-art deep reinforcement learning agents, with reasonable computation cost.
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