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EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search

Published: 14 August 2022 Publication History

Abstract

Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on items, plays a key fundamental role in sponsored search. E-commerce platforms display organic search results and advertisements (ads), collectively called items, together as a mixed list. The items displayed around the predicted ad, i.e. external items, may affect the user clicking on the predicted. Previous CTR models assume the user click only relies on the ad itself, which overlooks the effects of external items, referred to as external effects, or externalities. During the advertising prediction, the organic results have been generated by the organic system, while the final displayed ads on multiple ad slots have not been figured out, which leads to two challenges: 1) the predicted (target) ad may win any ad slot, bringing about diverse externalities. 2) external ads are undetermined, resulting in incomplete externalities. Facing the above challenges, inspired by the Transformer, we propose EXternality TRansformer (EXTR) which regards target ad with all slots as query and external items as key&value to model externalities in all exposure situations in parallel. Furthermore, we design a Potential Allocation Generator (PAG) for EXTR, to learn the allocation of potential external ads to complete the externalities. Extensive experimental results on Alibaba datasets demonstrate the effectiveness of externalities in the task of CTR prediction and illustrate that our proposed approach can bring significant profits to the real-world e-commerce platform. EXTR now has been successfully deployed in the online search advertising system in Alibaba, serving the main traffic.

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Cited By

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  • (2024)It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding RepresentationJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1901000819:1(135-151)Online publication date: 12-Jan-2024
  • (2024)Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale RecommendersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688041(829-831)Online publication date: 8-Oct-2024
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    cover image ACM Conferences
    KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    August 2022
    5033 pages
    ISBN:9781450393850
    DOI:10.1145/3534678
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    Published: 14 August 2022

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    Author Tags

    1. click-through rate prediction
    2. deep learning
    3. online advertising

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    View all
    • (2024)It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding RepresentationJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1901000819:1(135-151)Online publication date: 12-Jan-2024
    • (2024)Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale RecommendersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688041(829-831)Online publication date: 8-Oct-2024
    • (2024)Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce SearchProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680063(5007-5014)Online publication date: 21-Oct-2024
    • (2024)Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in FeedProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657774(1211-1220)Online publication date: 10-Jul-2024
    • (2023)TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer BehaviorJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1803007018:3(1404-1418)Online publication date: 17-Aug-2023
    • (2023)PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerceProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599886(4823-4831)Online publication date: 6-Aug-2023
    • (2023)Learning-Based Ad Auction Design with Externalities: The Framework and A Matching-Based ApproachProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599403(1291-1302)Online publication date: 6-Aug-2023
    • (2023)MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel FeedProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592018(2159-2163)Online publication date: 19-Jul-2023
    • (2023)Context-Aware Modeling via Simulated Exposure Page for CTR PredictionProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591967(1904-1908)Online publication date: 19-Jul-2023
    • (2023)Unveiling E-Commerce User Behavior through Deep Learning Evolutionary Data Mining2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)10.1109/RMKMATE59243.2023.10369373(1-7)Online publication date: 1-Nov-2023
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