期刊
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
卷 -, 期 -, 页码 7942-7946出版社
IEEE
DOI: 10.1109/ICASSP43922.2022.9747149
关键词
News recommendation; multi-interest modeling; session-based recommendation
资金
- National Natural Science Foundation of China [61502259, 11901325]
- National Key R&D Program of China [2018YFC0831700]
- Key Program of Science and Technology of Shandong Province [2020CXGC010901, 2019JZZY020124]
The paper proposes a multi-interest news sequence (MINS) model for news recommendation, which can better distinguish and model the potential multiple interests of a user, thus improving the accuracy of news recommendation.
A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session. Generally, a user's interests are diverse, namely there are multiple interests corresponding to different types of news, e.g., news of distinct topics, within a session. However, most of existing methods typically overlook such important characteristic and thus fail to distinguish and model the potential multiple interests of a user, impeding accurate recommendation of the next piece of news. Therefore, this paper proposes multi-interest news sequence (MINS) model for news recommendation. In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations. The experimental results on a real-world dataset demonstrate that our model can achieve better performance than the state-of-the-art compared models. Our source code is publicly available on GitHub (1).
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