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Deep Context Interest Network for Click-Through Rate Prediction

Published: 21 October 2023 Publication History

Abstract

Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus on user behavior modeling to improve CTR prediction performance. However, most of those methods only model users' positive interests from users' click items while ignoring the context information, which is the display items around the clicks, resulting in inferior performance. In this paper, we highlight the importance of context information on user behavior modeling and propose a novel model named Deep Context Interest Network (DCIN), which integrally models the click and its display context to learn users' context-aware interests. DCIN consists of three key modules: 1) Position-aware Context Aggregation Module (PCAM), which performs aggregation of display items with an attention mechanism; 2) Feedback-Context Fusion Module (FCFM), which fuses the representation of clicks and display contexts through non-linear feature interaction; 3) Interest Matching Module (IMM), which activates interests related with the target item. Moreover, we provide our hands-on solution to implement DCIN on large-scale industrial systems. The significant improvements in both offline and online evaluations demonstrate the superiority of our proposed DCIN method. Notably, DCIN has been deployed on our online advertising system serving the main traffic, which brings 1.5% CTR and 1.5% RPM lift.

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    cover image ACM Conferences
    CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
    October 2023
    5508 pages
    ISBN:9798400701245
    DOI:10.1145/3583780
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    Published: 21 October 2023

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    Author Tags

    1. context-aware interests
    2. ctr prediction
    3. user behavior modeling

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