short-paper

Ad Click Prediction in Sequence with Long Short-Term Memory Networks: an Externality-aware Model

Authors:

Xiaoliang Ling,

Eren Manavoglu,

Qi ZhangAuthors Info & Claims

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Pages 1065 - 1068

https://doi.org/10.1145/3209978.3210071

Published: 27 June 2018 Publication History

Abstract

Ad click prediction is a task to estimate the click-through rate (CTR) in sponsored ads, the accuracy of which impacts user search experience and businesses' revenue. State-of-the-art sponsored search systems typically model it as a classification problem and employ machine learning approaches to predict the CTR per ad. In this paper, we propose a new approach to predict ad CTR in sequence which considers user browsing behavior and the impact of top ads quality to the current one. To the best of our knowledge, this is the first attempt in the literature to predict ad CTR by using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed model is evaluated on a real dataset and we show that LSTM-RNN outperforms DNN model on both AUC and RIG. Since the RNN inference is time consuming, a simplified version is also proposed, which can achieve more than half of the gain with the overall serving cost almost unchanged.

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Cited By

Chen HBei YShen QXu YZhou SHuang WHuang FWang SHuang XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Macro Graph Neural Networks for Online Billion-Scale Recommender SystemsProceedings of the ACM Web Conference 202410.1145/3589334.3645517(3598-3608)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645517
Ren YHan XZhao XZhang SZhang YChua TLauw HSi LTerzi ETsaparas P(2023)Slate-Aware Ranking for RecommendationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570380(499-507)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570380
Yang YZhai P(2022)Click-through rate prediction in online advertising: A literature reviewInformation Processing & Management10.1016/j.ipm.2021.10285359:2(102853)Online publication date: Mar-2022
https://doi.org/10.1016/j.ipm.2021.102853
Show More Cited By

Index Terms

Ad Click Prediction in Sequence with Long Short-Term Memory Networks: an Externality-aware Model
1. Information systems
  1. World Wide Web
    1. Online advertising
      1. Sponsored search advertising

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Published In

cover image ACM Conferences

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

June 2018

1509 pages

ISBN:9781450356572

DOI:10.1145/3209978

General Chairs:
Kevyn Collins-Thompson
University of Michigan, United States
,
Qiaozhu Mei
University of Michigan, United States
,
Program Chairs:
Brian Davison
Lehigh University, United States
,
Yiqun Liu
Tsinghua University, China
,
Emine Yilmaz
University College London, United Kingdom

Copyright © 2018 ACM.

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 the author(s) 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2018

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SIGIR '18

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SIGIR

SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval

July 8 - 12, 2018

MI, Ann Arbor, USA

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SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Chen HBei YShen QXu YZhou SHuang WHuang FWang SHuang XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Macro Graph Neural Networks for Online Billion-Scale Recommender SystemsProceedings of the ACM Web Conference 202410.1145/3589334.3645517(3598-3608)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645517
Ren YHan XZhao XZhang SZhang YChua TLauw HSi LTerzi ETsaparas P(2023)Slate-Aware Ranking for RecommendationProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570380(499-507)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570380
Yang YZhai P(2022)Click-through rate prediction in online advertising: A literature reviewInformation Processing & Management10.1016/j.ipm.2021.10285359:2(102853)Online publication date: Mar-2022
https://doi.org/10.1016/j.ipm.2021.102853
Gwadabe TLiu Y(2022)IC-GAR: item co-occurrence graph augmented session-based recommendationNeural Computing and Applications10.1007/s00521-021-06859-x34:10(7581-7596)Online publication date: 8-Jan-2022
https://doi.org/10.1007/s00521-021-06859-x
Gharibshah ZZhu XHainline AConway M(2020)Deep Learning for User Interest and Response Prediction in Online Display AdvertisingData Science and Engineering10.1007/s41019-019-00115-yOnline publication date: 17-Jan-2020
https://doi.org/10.1007/s41019-019-00115-y
Shi XZhang TDong WDeng Z(2019)An Embedded Model XG-FwFMs for Click-Through RateProceedings of the 4th International Conference on Big Data and Computing10.1145/3335484.3335538(179-184)Online publication date: 10-May-2019
https://dl.acm.org/doi/10.1145/3335484.3335538

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