research-article

CL4DIV: A Contrastive Learning Framework for Search Result Diversification

Authors:

Ji-Rong WenAuthors Info & Claims

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Pages 171 - 180

https://doi.org/10.1145/3616855.3635851

Published: 04 March 2024 Publication History

Abstract

Search result diversification aims to provide a diversified document ranking list so as to cover as many intents as possible and satisfy the various information needs of different users. Existing approaches usually represented documents by pretrained embeddings (such as doc2vec and Glove). These document representations cannot adequately represent the document's content and are hard to capture the intrinsic user's intent coverage of the given query. Moreover, the limited number of labeled data for search result diversification exacerbates the difficulty of obtaining more efficient document representations. To alleviate these problems and learn more effective document representations, we propose a Contrastive Learning framework for search result DIVersification (CL4DIV). Specifically, we design three contrastive learning tasks from the perspective of subtopics, documents, and candidate document sequences, which correspond to three essential elements in search result diversification. These training tasks are employed to pretrain the document encoder and the document sequence encoder, which are used in the diversified ranking model. Experimental results show that øurs significantly outperforms all existing diversification models. Further analysis demonstrates that our method has wide applicability and can also be used to improve several existing methods.

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Cited By

Deng ZDou ZZhu YQin XCheng PWu JWang HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)JDivPS: A Diversified Product Search DatasetProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657888(1152-1161)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657888

Index Terms

CL4DIV: A Contrastive Learning Framework for Search Result Diversification
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Information retrieval diversity

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cover image ACM Conferences

WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining

March 2024

1246 pages

ISBN:9798400703713

DOI:10.1145/3616855

General Chairs:
Luz Angélica
Caudillo Mata (MDA Geointelligence)
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Silvio Lattanzi
Google Research
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Andrés Muñoz Medina
Google Research
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Program Chairs:
Leman Akoglu
CMU
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Aristides Gionis
KTH
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Sergei Vassilvitskii
Google Research

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Deng ZDou ZZhu YQin XCheng PWu JWang HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)JDivPS: A Diversified Product Search DatasetProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657888(1152-1161)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657888

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