research-article

Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism

Authors:

Zhicheng DouAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 1464 - 1473

https://doi.org/10.1145/3589334.3645482

Published: 13 May 2024 Publication History

Abstract

Traditional search engines usually provide identical search results for all users, overlooking individual preferences. To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs. Deep learning-based personalized search methods have shown promise, but they rely heavily on abundant training data, making them susceptible to data sparsity challenges. This paper proposes a Cognitive Personalized Search (CoPS) model, which integrates Large Language Models (LLMs) with a cognitive memory mechanism inspired by human cognition. CoPS employs LLMs to enhance user modeling and user search experience. The cognitive memory mechanism comprises sensory memory for quick sensory responses, working memory for sophisticated cognitive responses, and long-term memory for storing historical interactions. CoPS handles new queries using a three-step approach: identifying re-finding behaviors, constructing user profiles with relevant historical information, and ranking documents based on personalized query intent. Experiments show that CoPS outperforms baseline models in zero-shot scenarios.

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Cited By

Yalın BAkyuz AAbut FAkay MUlus C(2024)Design and Development of a Fashion Oriented Personalized Search EngineCukurova University Journal of Natural and Applied Sciences10.70395/cunas.15151783:2(36-44)Online publication date: 11-Dec-2024
https://doi.org/10.70395/cunas.1515178
Shi YXu MZhang HZi XWu QJi WFei HZheng ZFei HWei YZheng Z(2024)A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search EngineProceedings of the 2nd International Workshop on Deep Multimodal Generation and Retrieval10.1145/3689091.3690087(12-20)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689091.3690087
Kato MKando NClarke CLiu YSakai TIshita EOhshima HHasibi FMao JJose J(2024)The First Workshop on Evaluation Methodologies, Testbeds and Community for Information Access Research (EMTCIR 2024)Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698434(311-314)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698434
Show More Cited By

Index Terms

Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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Published In

cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

Copyright © 2024 ACM.

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New York, NY, United States

Publication History

Published: 13 May 2024

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National Natural Science Foundation of China
National Key R&D Program of China
Beijing Natural Science Foundation

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WWW '24

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SIGWEB

WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Yalın BAkyuz AAbut FAkay MUlus C(2024)Design and Development of a Fashion Oriented Personalized Search EngineCukurova University Journal of Natural and Applied Sciences10.70395/cunas.15151783:2(36-44)Online publication date: 11-Dec-2024
https://doi.org/10.70395/cunas.1515178
Shi YXu MZhang HZi XWu QJi WFei HZheng ZFei HWei YZheng Z(2024)A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search EngineProceedings of the 2nd International Workshop on Deep Multimodal Generation and Retrieval10.1145/3689091.3690087(12-20)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689091.3690087
Kato MKando NClarke CLiu YSakai TIshita EOhshima HHasibi FMao JJose J(2024)The First Workshop on Evaluation Methodologies, Testbeds and Community for Information Access Research (EMTCIR 2024)Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698434(311-314)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698434
Ai QDou ZZhang M(2024)Improving Generative Information Retrieval Systems Based on User FeedbackInformation Access in the Era of Generative AI10.1007/978-3-031-73147-1_5(111-133)Online publication date: 12-Sep-2024
https://doi.org/10.1007/978-3-031-73147-1_5

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