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10 August 2023 News recommendation model based on user implicit feedback noise processing
Lisheng Zhang, Zhuangzhuang Wang
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Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127482P (2023) https://doi.org/10.1117/12.2689848
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China
Abstract
Most news recommendation methods believe that all news in the implicit feedback sequence is beneficial to the final result, but it includes the influence of irrelevant news (i.e. false user behavior). This is not ideal for mining user interest information, which usually contains large noise. In order to address this issue, a news recommendation model based on positive and negative implicit feedback feature fusion with sparse attention mechanism is proposed. Firstly, the method adopts the method based on deep learning. In the user interest representation module, the adaptive sparse transformation function is introduced to eliminate the influence of irrelevant news in the user's positive and negative implicit feedback sequence. Secondly, the user's interest features are better constructed by fusing the user's positive and negative implicit feedback features. Finally, the proposed model is examined and analyzed on the MIND dataset. Compared with other news recommendation methods, the recommendation evaluation index of this method has been effectively improved.
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Lisheng Zhang and Zhuangzhuang Wang "News recommendation model based on user implicit feedback noise processing", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127482P (10 August 2023); https://doi.org/10.1117/12.2689848
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KEYWORDS
Negative feedback
Positive feedback
Data modeling
Neural networks
Modeling
Semantics
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