Abstract
News recommendation techniques aim to recommend candidate news to target user that he may be interested in, according to his browsed historical news. At present, existing works usually tend to represent user reading interest using a single vector during the modeling procedure. Actually, it is obviously that users usually have multiple and diverse interest in reality, such as sports, entertainment and so on. Thus it is irrational to represent user sophisticated semantic interest simply utilizing a single vector, which may conceal fine-grained information. In this work, we propose a novel method combining multi-interest extraction with contrastive learning, named MIECL, to tackle the above problem. Specifically, first, we construct several interest prototypes and design a multi-interest user encoder to learn multiple user representations under different interest conditions simultaneously. Then we adopt a graph-enhanced user encoder to enrich user corresponding semantic representation under each interest background through aggregating relevant information from neighbors. Finally, we contrast user multi-interest representations and interest prototype vectors to optimize the user representations themselves, in order to promote dissimilar semantic interest away from each other. We conduct experiments on two real-world news recommendation datasets MIND-Large, MIND-Small and empirical results demonstrate the effectiveness of our approach from multiple perspectives.
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Notes
- 1.
Our source code is available at https://github.com/wangsc2113/MIECL..
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A small version of the MIND-Large dataset by randomly sampling 50,000 users and their behavior logs.
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Due to the limitation of computer resources, we did not use the pretrained language models to encode the news titles and compare with baselines based on pretrained models.
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Acknowledgements
We sincerely thank all the anonymous reviewers for their comments and suggestions. This work is supported by the National Key Research and Development Program of China (grant No.2021YFB3100600), the National Natural Science Foundation of China (No.62106059), the Strategic Priority Research Program of Chinese Academy of Sciences (grant No.XDC02040400), , and the Youth Innovation Promotion Association of CAS (Grant No. 2021153).
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Wang, S., Guo, S., Wang, L., Liu, T., Xu, H. (2023). Multi-interest Extraction Joint with Contrastive Learning for News Recommendation. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_37
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