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Multi-Aspect Dense Retrieval

Authors:

Swaraj Khadanga,

Shaleen Kumar Gupta,

Mingyang Zhang,

Michael BenderskyAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3178 - 3186

https://doi.org/10.1145/3534678.3539137

Published: 14 August 2022 Publication History

Abstract

Prior work in Dense Retrieval usually encodes queries and documents using single-vector representations (also called embeddings) and performs retrieval in the embedding space using approximate nearest neighbor search. This paradigm enables efficient semantic retrieval. However, the single-vector representations can be ineffective at capturing different aspects of the queries and documents in relevance matching, especially for some vertical domains. For example, in e-commerce search, these aspects could be category, brand and color. Given a query ''white nike socks", a Dense Retrieval model may mistakenly retrieve some ''white adidas socks" while missing out the intended brand. We propose to explicitly represent multiple aspects using one embedding per aspect. We introduce an aspect prediction task to teach the model to capture aspect information with particular aspect embeddings. We design a lightweight network to fuse the aspect embeddings for representing queries and documents. Our evaluation using an e-commerce dataset shows impressive improvements over strong Dense Retrieval baselines. We also discover that the proposed aspect embeddings can enhance the interpretability of Dense Retrieval models as a byproduct.

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Cited By

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Index Terms

Multi-Aspect Dense Retrieval
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 14 August 2022

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KDD '22

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

Washington DC, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Huang YHuang J(2024)Exploring ChatGPT for next-generation information retrieval: Opportunities and challengesWeb Intelligence10.3233/WEB-23036322:1(31-44)Online publication date: 26-Mar-2024
https://doi.org/10.3233/WEB-230363
Chen HDou ZHao XTao YSong SSheng ZBaeza-Yates RBonchi F(2024)Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-trainingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671513(4839-4849)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671513
Li MChen XXiang JZhang QMa CDai CChang JLiu ZZhang GAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Multi-Intent Attribute-Aware Text Matching in SearchingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635813(360-368)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635813
Sun XBi KGuo JYang SZhang QLiu ZZhang GCheng XAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense RetrievalProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635770(674-682)Online publication date: 4-Mar-2024
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Chang HAgarwal NMcCallum AAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential RecommendersProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635755(67-76)Online publication date: 4-Mar-2024
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Li WSun RZhang TWu Y(2024)Going Beyond Passages: Readability Assessment for Book-Level Long TextsChinese Computational Linguistics10.1007/978-981-97-8367-0_26(434-450)Online publication date: 29-Nov-2024
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Bi KSun XGuo JCheng X(2024)Reproducibility Analysis and Enhancements for Multi-aspect Dense Retriever with Aspect LearningAdvances in Information Retrieval10.1007/978-3-031-56066-8_17(194-209)Online publication date: 24-Mar-2024
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Lan TMao XWei WGao XHuang H(2023)Towards Efficient Coarse-grained Dialogue Response SelectionACM Transactions on Information Systems10.1145/359760942:2(1-32)Online publication date: 27-Sep-2023
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Sun XBi KGuo JMa XFan YShan HZhang QLiu ZFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense RetrievalProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615157(4300-4304)Online publication date: 21-Oct-2023
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