research-article

USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence

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Ji-Rong WenAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 2373 - 2382

https://doi.org/10.1145/3459637.3482489

Published: 30 October 2021 Publication History

Abstract

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently -- they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information SEarch and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way. Experiments on a dataset from a real-world information content service platform verify that our model outperforms separate search and recommendation baselines.

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Cited By

Zhang YWu YHan RSun YZhu YLi XLin WZhuang FAn ZXu YBaeza-Yates RBonchi F(2024)Unified Dual-Intent Translation for Joint Modeling of Search and RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671519(6291-6300)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671519
Liu JChen QXu JLi JLi BXu SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerceProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661356(2890-2894)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661356
Shi TSi ZXu JZhang XZang XZheng KLeng DNiu YSong YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)UniSAR: Modeling User Transition Behaviors between Search and RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657811(1029-1039)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657811
Show More Cited By

Index Terms

USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Web search engines

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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Intelligent Social Governance Platform, Major Innovation & Planning Interdisciplinary Platform for the ?Double-First Class? Initiative, Renmin University of China
Shandong Provincial Natural Science Foundation
Beijing Outstanding Young Scientist Program
National Natural Science Foundation of China

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Cited By

Zhang YWu YHan RSun YZhu YLi XLin WZhuang FAn ZXu YBaeza-Yates RBonchi F(2024)Unified Dual-Intent Translation for Joint Modeling of Search and RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671519(6291-6300)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671519
Liu JChen QXu JLi JLi BXu SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerceProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661356(2890-2894)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661356
Shi TSi ZXu JZhang XZang XZheng KLeng DNiu YSong YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)UniSAR: Modeling User Transition Behaviors between Search and RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657811(1029-1039)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657811
Zheng ZHu XGao SZhu HXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)MIRROR: A Multi-View Reciprocal Recommender System for Online RecruitmentProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657776(543-552)Online publication date: 10-Jul-2024
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Sun ZSi ZZhang XZang XSong YXu HXu JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes ProcessProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657732(1018-1028)Online publication date: 10-Jul-2024
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Xie JLiu SCong GChen ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)UnifiedSSR: A Unified Framework of Sequential Search and RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645427(3410-3419)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645427
Wu NGong MShou LPei JJiang DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615498(4871-4878)Online publication date: 21-Oct-2023
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Sun ZSi ZZang XLeng DNiu YSong YZhang XXu JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)KuaiSAR: A Unified Search And Recommendation DatasetProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615123(5407-5411)Online publication date: 21-Oct-2023
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Gong YDing XSu YShen KLiu ZZhang GFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)An Unified Search and Recommendation Foundation Model for Cold-Start ScenarioProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614657(4595-4601)Online publication date: 21-Oct-2023
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Si ZSun ZZhang XXu JSong YZang XWen J(2023)Enhancing Recommendation with Search Data in a Causal Learning MannerACM Transactions on Information Systems10.1145/358242541:4(1-31)Online publication date: 8-Apr-2023
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