research-article

Integrating Representation and Interaction for Context-Aware Document Ranking

Authors:

Ji-Rong WenAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 1

Article No.: 21, Pages 1 - 23

https://doi.org/10.1145/3529955

Published: 10 January 2023 Publication History

Abstract

Recent studies show that historical behaviors (such as queries and their clicks) contained in a search session can benefit the ranking performance of subsequent queries in the session. Existing neural context-aware ranking models usually rank documents based on either latent representations of user search behaviors or the word-level interactions between the candidate document and each historical behavior in the search session. However, these two kinds of models both have their own drawbacks. Representation-based models neglect fine-grained information on word-level interactions, whereas interaction-based models suffer from the length restriction of session sequence because of the large cost of word-level interactions. To complement the limitations of these two kinds of models, we propose a unified context-aware document ranking model that takes full advantage of both representation and interaction. Specifically, instead of matching a candidate document with every single historical query in a session, we encode the session history into a latent representation and use this representation to enhance the current query and the candidate document. We then just match the enhanced query and candidate document with several matching components to capture the fine-grained information of word-level interactions. Rich experiments on two public query logs prove the effectiveness and efficiency of our model for leveraging representation and interaction.

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Index Terms

Integrating Representation and Interaction for Context-Aware Document Ranking
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 1

January 2023

759 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3570137

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 10 January 2023

Online AM: 14 April 2022

Accepted: 30 March 2022

Revised: 22 February 2022

Received: 10 September 2021

Published in TOIS Volume 41, Issue 1

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

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https://dl.acm.org/doi/10.1145/3675163
Liang YYang EGuo GCai WJiang LWang X(2024)Deconfounding User Preference in Recommendation Systems through Implicit and Explicit FeedbackACM Transactions on Knowledge Discovery from Data10.1145/3673762Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3673762
Zhao YWang YLiu YCheng XAggarwal CDerr T(2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/3664928Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3664928
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Zhu YYi JXie JChen Z(2024)Deep Causal Reasoning for RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/365398515:4(1-25)Online publication date: 18-Jun-2024
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