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Graph-aware collaborative reasoning for click-through rate prediction

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Abstract

Click-through rate prediction(CTR) is a critical task in an online advertising system. Recently, deep learning based architectures have brought great attention in Click-through rate prediction by learning the nonlinear interaction between feature embedding of users and items. However, these methods have the following issues: (1) The collaborative information between users and items could not be fully explored due to the static embedding with lookup-table technique. (2) The learning procedure lacks cognitive reasoning about what the users want to do and what they may need. To address the above challenges, we propose a graph aware collaborative reasoning method for CTR prediction which explores the collaborative information with graph and then predicts the users’ behaviors with logical reasoning. Specifically, the graph is built by the common behaviors between users, and the embedding of users and items can be learned by propagating the collaborative information in the graph. Then with the collaborative embedding of users and items, two logical operations NOT and OR are adopted to integrate the embedding for logical reasoning with the neural networks. By learning the proposed architecture in an end-to-end manner, the logical behaviors of users in the behavior sequences can be learned efficiently. Extensive experiments on five real-world datasets show that the proposed method outperforms several state-of-the-art methods in CTR prediction.

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Data availability

The datasets analyses during the current study are available in the “http://jmcauley.ucsd.edu/data/amazon/”.

Code availability

Not applicable.

Notes

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

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 62006176, 62141112, 41871243, the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) under Grant 2019AEA170,  and the Natural Science Foundation of Hubei Province under Grants 2020CFB241. The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.

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  1. School of Computer Science, Wuhan University, Wuhan, 430000, China

    Xin Zhang, Zengmao Wang & Bo Du

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  1. Xin Zhang
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  2. Zengmao Wang
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Three authors contributed equally to the data analysis, model construction, experiments and manuscript in this work.

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Correspondence to Zengmao Wang.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of the manuscript.

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Zengmao Wang and Bo Du contributed equally to this work.

This article belongs to the Topical Collection: Special Issue on Spatiotemporal Data Management and Analytics for Recommend

Guest Editors: Shuo Shang, Xiangliang Zhang and Panos Kalnis

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Zhang, X., Wang, Z. & Du, B. Graph-aware collaborative reasoning for click-through rate prediction. World Wide Web 26, 967–987 (2023). https://doi.org/10.1007/s11280-022-01050-1

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