Satisfaction-Aware User Interest Network for Click-Through Rate Prediction
Authors: 
Mao Pan, Wen Shi, Kun Zhou, Zheng Li, Dongyue Wang, Zhuoye Ding, Xiwei Zhao, Sulong XuAuthors Info & Claims
Pages 4234 - 4238
Abstract
Click-Through Rate (CTR) prediction plays a pivotal role in numerous industrial applications, including online advertising and recommender systems. Existing approaches primarily focus on modeling the correlation between user interests and candidate items. However, we argue that personalized user preferences for candidate items depend not only on correlation but also on the satisfaction of associated interests. To address this limitation, we propose SUIN, a novel CTR model that integrates satisfaction factors into user interest modeling for enhanced click-through rate prediction. Specifically, we employ a user interest satisfaction-aware network to capture the degree of satisfaction for each interest, thereby enabling adaptation of the user's personalized preference based on satisfaction levels. Additionally, we leverage the exposure-unclicked signal (recommended to the user but not clicked) as supervision during training, facilitating the interest satisfaction module to better model the satisfaction degree of user interests. Besides, this module serves as a foundational building block suitable for integration into mainstream sequential-based CTR models. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed model, outperforming state-of-the-art methods across various evaluation metrics. Furthermore, an online A/B test deployed on large-scale recommender systems shows significant improvements achieved by our model in diverse evaluation metrics.
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Index Terms
- Satisfaction-Aware User Interest Network for Click-Through Rate Prediction
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Published In
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
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- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
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- Min Zhang,
- Rodrygo Santos
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Published: 21 October 2023
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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
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