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Lookup NU author(s): Dr Huizhi Liang
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2022.
For re-use rights please refer to the publisher's terms and conditions.
Recommender systems are popular for personalization in online communities. Users, items, and other affiliated information such as tags, item genres, and user friends of an online community form a heterogenous information network. User profiling is the foundation of personalized recommender systems. It provides the basis to discover knowledge about an individual user's interests to items. Typically, users are profiled with their direct explicit or implicit ratings, which ignored the inter-connections among users, items, and other entity nodes of the information network. This paper proposes a deep reinforcement user profiling approach for recommender systems. The user profiling process is framed as a sequential decision making problem which can be solved with a Reinforcement Learning (RL) agent. The RL agent interacts with the external heterogenous information network environment and learns a decision making policy network to decide whether there is an interest or preference path between a user and an unobserved item. To effectively train the RL agent, this paper proposes a multi-iteration training process to combine both expert and data-specific knowledge to profile users, generate meta-paths, and make recommendations. The effectiveness of the proposed approaches is demonstrated in experiments conducted on three datasets.
Author(s): Liang H
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Knowledge and Data Engineering
Year: 2022
Volume: 34
Issue: 4
Pages: 1723-1734
Print publication date: 01/04/2022
Online publication date: 29/05/2020
Acceptance date: 20/05/2020
Date deposited: 06/01/2022
ISSN (print): 1041-4347
ISSN (electronic): 1558-2191
Publisher: IEEE
URL: https://doi.org/10.1109/TKDE.2020.2998695
DOI: 10.1109/TKDE.2020.2998695
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