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Core Interest Network for Click-Through Rate Prediction

Published: 04 January 2021 Publication History

Abstract

In modern online advertising systems, the click-through rate (CTR) is an important index to measure the popularity of an item. It refers to the ratio of users who click on a specific advertisement to the number of total users who view it. Predicting the CTR of an item in advance can improve the accuracy of the advertisement recommendation. And it is commonly calculated based on users’ interests. Thus, extracting users’ interests is of great importance in CTR prediction tasks. In the literature, a lot of studies treat the interaction between users and items as sequential data and apply the recurrent neural network (RNN) model to extract users’ interests. However, these solutions cannot handle the case when the sequence length is relatively long, e.g., over 100. This is because of the vanishing gradient problem of RNN, i.e., the model cannot learn a users’ previous behaviors that are too far away from the current moment. To address this problem, we propose a new Core Interest Network (CIN) model to mitigate the problem of a long sequence in the CTR prediction task with sequential data. In brief, we first extract the core interests of users and then use the refined data as the input of subsequent learning tasks. Extensive evaluations on real dataset show that our CIN model can outperform the state-of-the-art solutions in terms of prediction accuracy.

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    Published In

    cover image ACM Transactions on Knowledge Discovery from Data
    ACM Transactions on Knowledge Discovery from Data  Volume 15, Issue 2
    Survey Paper and Regular Papers
    April 2021
    524 pages
    ISSN:1556-4681
    EISSN:1556-472X
    DOI:10.1145/3446665
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 04 January 2021
    Accepted: 01 October 2020
    Revised: 01 August 2020
    Received: 01 February 2020
    Published in TKDD Volume 15, Issue 2

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    Author Tags

    1. CTR prediction
    2. computational advertising
    3. sequential recommendation
    4. time series prediction
    5. user portrait

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    Funding Sources

    • National Science Fund for Distinguished Young Scholars
    • National Key R8D Program of China
    • Fundamental Research Funds for the Central Universities
    • National Natural Science Foundation of China
    • Natural Science Basic Research Plan in Shaanxi Province of China

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