research-article

A Personalized Dense Retrieval Framework for Unified Information Access

Authors:

Surya Kallumadi,

Rodrigo Nogueira,

Hamed ZamaniAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 121 - 130

https://doi.org/10.1145/3539618.3591626

Published: 18 July 2023 Publication History

Abstract

Developing a universal model that can efficiently and effectively respond to a wide range of information access requests-from retrieval to recommendation to question answering---has been a long-lasting goal in the information retrieval community. This paper argues that the flexibility, efficiency, and effectiveness brought by the recent development in dense retrieval and approximate nearest neighbor search have smoothed the path towards achieving this goal. We develop a generic and extensible dense retrieval framework, called framework, that can handle a wide range of (personalized) information access requests, such as keyword search, query by example, and complementary item recommendation. Our proposed approach extends the capabilities of dense retrieval models for ad-hoc retrieval tasks by incorporating user-specific preferences through the development of a personalized attentive network. This allows for a more tailored and accurate personalized information access experience. Our experiments on real-world e-commerce data suggest the feasibility of developing universal information access models by demonstrating significant improvements even compared to competitive baselines specifically developed for each of these individual information access tasks. This work opens up a number of fundamental research directions for future exploration.

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Cited By

Zhou XGao YJie XCai XBu JWang HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EASE-DR: Enhanced Sentence Embeddings for Dense RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657925(2374-2378)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657925
Salemi AKallumadi SZamani HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Optimization Methods for Personalizing Large Language Models through Retrieval AugmentationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657783(752-762)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657783
Zeng HLuo CZamani HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous DecodingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657746(469-480)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657746
Show More Cited By

Index Terms

A Personalized Dense Retrieval Framework for Unified Information Access
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Retrieval models and ranking
      1. Learning to rank

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Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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Publication History

Published: 18 July 2023

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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

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Cited By

Zhou XGao YJie XCai XBu JWang HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EASE-DR: Enhanced Sentence Embeddings for Dense RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657925(2374-2378)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657925
Salemi AKallumadi SZamani HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Optimization Methods for Personalizing Large Language Models through Retrieval AugmentationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657783(752-762)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657783
Zeng HLuo CZamani HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous DecodingProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657746(469-480)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657746
Xu ZChen DKuang JYi ZLi YShen YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support ConversationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657695(774-784)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657695
Zeng HLuo CJin BSarwar SWei TZamani HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Scalable and Effective Generative Information RetrievalProceedings of the ACM Web Conference 202410.1145/3589334.3645477(1441-1452)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645477
He XWei THe JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Robust Basket Recommendation via Noise-tolerated Graph Contrastive LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615039(709-719)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615039

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