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A knowledge-enhanced interest segment division attention network for click-through rate prediction

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Abstract

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN.

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Data availability

The data used in this article are available in the online supplementary material. Supplementary materials are available at http://jmcauley.ucsd.edu/data/amazon/, https://grouplens.org/datasets/movielens/1m/ and https://grouplens.org/datasets/hetrec-2011/.

Code availability

The code for this paper has been uploaded to Github: https://github.com/java-jay/KISDAN.

Notes

  1. http://jmcauley.ucsd.edu/data/amazon/.

  2. https://grouplens.org/datasets/movielens/1m/.

  3. https://grouplens.org/datasets/hetrec-2011/.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61672158, 61972097 and U21A20472, in part by the Major Science and Technology project of Fujian Province (China) under Granted No. 2021HZ022007, in part by the Industry-Academy Cooperation Project under Grant 2021H6022, in part by the Natural Science Foundation of Fujian Province under Grant 2020J01494, in part by the Collaborative Innovation Platform Project of Fuzhou City under Grant 2023-P-002.

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Authors and Affiliations

  1. College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, Fujian Province, China

    Zhanghui Liu, Shijie Chen, Yuzhong Chen, Jieyang Su, Jiayuan Zhong & Chen Dong

  2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, 350108, Fujian Province, China

    Zhanghui Liu, Shijie Chen, Yuzhong Chen, Jieyang Su, Jiayuan Zhong & Chen Dong

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  1. Zhanghui Liu
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Correspondence to Yuzhong Chen.

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Liu, Z., Chen, S., Chen, Y. et al. A knowledge-enhanced interest segment division attention network for click-through rate prediction. Neural Comput & Applic 36, 21817–21837 (2024). https://doi.org/10.1007/s00521-024-10330-y

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