research-article

Image Matters: Visually Modeling User Behaviors Using Advanced Model Server

Authors:

Zhiqiang Zhang,

Kun GaiAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 2087 - 2095

https://doi.org/10.1145/3269206.3272007

Published: 17 October 2018 Publication History

Abstract

In Taobao, the largest e-commerce platform in China, billions of items are provided and typically displayed with their images.For better user experience and business effectiveness, Click Through Rate (CTR) prediction in online advertising system exploits abundant user historical behaviors to identify whether a user is interested in a candidate ad. Enhancing behavior representations with user behavior images will help understand user's visual preference and improve the accuracy of CTR prediction greatly. So we propose to model user preference jointly with user behavior ID features and behavior images. However, training with user behavior images brings tens to hundreds of images in one sample, giving rise to a great challenge in both communication and computation. To handle these challenges, we propose a novel and efficient distributed machine learning paradigm called Advanced Model Server (AMS). With the well-known Parameter Server (PS) framework, each server node handles a separate part of parameters and updates them independently. AMS goes beyond this and is designed to be capable of learning a unified image descriptor model shared by all server nodes which embeds large images into low dimensional high level features before transmitting images to worker nodes. AMS thus dramatically reduces the communication load and enables the arduous joint training process. Based on AMS, the methods of effectively combining the images and ID features are carefully studied, and then we propose a Deep Image CTR Model. Our approach is shown to achieve significant improvements in both online and offline evaluations, and has been deployed in Taobao display advertising system serving the main traffic.

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Cited By

Alves Gomes MMeisen PMeisen T(2025)Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with EmbeddingsJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer2001001220:1(12)Online publication date: 16-Jan-2025
https://doi.org/10.3390/jtaer20010012
Alves Gomes MMeyes RMeisen PMeisen T(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
https://doi.org/10.3390/jtaer19010008
Xu ELi XZhou ZJi JZhao JMiao DWang SLiu LXu SSerra ESpezzano F(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
https://dl.acm.org/doi/10.1145/3627673.3680063
Show More Cited By

Index Terms

Image Matters: Visually Modeling User Behaviors Using Advanced Model Server
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Online advertising

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Published: 17 October 2018

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October 22 - 26, 2018

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Overall Acceptance Rate 1,466 of 6,316 submissions, 23%

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Cited By

Alves Gomes MMeisen PMeisen T(2025)Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with EmbeddingsJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer2001001220:1(12)Online publication date: 16-Jan-2025
https://doi.org/10.3390/jtaer20010012
Alves Gomes MMeyes RMeisen PMeisen T(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
https://doi.org/10.3390/jtaer19010008
Xu ELi XZhou ZJi JZhao JMiao DWang SLiu LXu SSerra ESpezzano F(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
https://dl.acm.org/doi/10.1145/3627673.3680063
Wen YChen STian YGuan WWang PDeng HXu JZheng BLi ZZou LLi CAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Unified Visual Preference Learning for User Intent UnderstandingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635858(816-825)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635858
Yang HYuan JYang SXu LYuan SZeng YChua TNgo CKumar RLauw HKa-Wei Lee R(2024)A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion ModelCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648315(180-189)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648315
Foniakov MBardukov AMakarov I(2024)Application of Multimodal Machine Learning for Image Recommendation SystemsRecent Trends in Analysis of Images, Social Networks and Texts10.1007/978-3-031-67008-4_18(235-249)Online publication date: 30-Jul-2024
https://doi.org/10.1007/978-3-031-67008-4_18
Hu XYu WWu YChen Y(2023)Multi-modal recommendation algorithm fusing visual and textual featuresPLOS ONE10.1371/journal.pone.028792718:6(e0287927)Online publication date: 29-Jun-2023
https://doi.org/10.1371/journal.pone.0287927
Chen XWang YYang J(2023)Multi-modal Recommendation based on Knowledge Graph2023 9th International Conference on Computer and Communications (ICCC)10.1109/ICCC59590.2023.10507494(2383-2388)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ICCC59590.2023.10507494
Wu HWang JZu Z(2023)Interaction-Assisted Multi-Modal Representation Learning for RecommendationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10095080(1-5)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10095080
Lin KZhang XLi FWang PLong QDeng HXu JZheng BAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Joint Optimization of Ad Ranking and Creative SelectionProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531855(2341-2346)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531855
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