Abstract: Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies suffer from two limitations: (1) considering the recommendation as a static procedure and ignoring the dynamic interactive nature between users and the recommender systems, (2) focusing on the immediate feedback of recommended items and neglecting the long-term rewards. To address the two limitations, in this paper we propose a novel recommendation framework based on deep reinforcement learning, called DRR. The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards. Furthermore, a state representation module is incorporated into DRR, which can explicitly capture the interactions between items and users. Three instantiation structures are developed. Extensive experiments on four real-world datasets are conducted under both the offline and online evaluation settings. The experimental results demonstrate the proposed DRR method indeed outperforms the state-of-the-art competitors.
| Subjects: | Information Retrieval (cs.IR) |
| Cite as: | arXiv:1810.12027 [cs.IR] |
| (or arXiv:1810.12027v3 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.1810.12027 |
Focus to learn more
arXiv-issued DOI via DataCiteFrom: Feng Liu [view email]
[v1] Mon, 29 Oct 2018 09:41:52 UTC (495 KB)
[v2] Tue, 30 Oct 2018 01:08:48 UTC (495 KB)
[v3] Tue, 29 Oct 2019 06:41:48 UTC (495 KB)
View a PDF of the paper titled Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling, by Feng Liu and 7 other authors