short-paper

CSR 2021: The 1st International Workshop on Causality in Search and Recommendation

Authors:

Yongfeng Zhang,

Xianjie ChenAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2677 - 2680

https://doi.org/10.1145/3404835.3462817

Published: 11 July 2021 Publication History

Abstract

Most of the current machine learning approaches to IR---including search and recommendation tasks---are mostly designed based on the basic idea of matching, which work from the perceptual and similarity learning perspective. This include both the learning of features from data such as representation learning, and the learning of similarity matching functions from data such as neural function learning. Though many models have been widely used in practical ranking systems such as search and recommendation, their design philosophy limits the models to the correlative signals in data. However, advancing from correlative learning to causal learning in search and recommendation is an important problem, because causal modeling can help us to think outside of the observational data for representation learning and ranking. More specially, causal learning can bring benefits to the IR community on various dimensions, including but not limited to Explainable IR models, Unbiased IR models, Fairness-aware IR models, Robust IR models and Cognitive Reasoning IR models. This workshop focuses on the research and application of causal modeling in search, recommendation and a broader scope of IR tasks. The workshop will gather both researchers and practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations on AI Ethics, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond. Workshop homepage is available online at https://csr21.github.io/.

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Cited By

Ruiz De villa ASottocornola GCoba LLucchesi FSkorulski B(2024)Ranking the causal impact of recommendations under collider bias in k-spots recommender systemsACM Transactions on Recommender Systems10.1145/36431392:2(1-29)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3643139
Tan JXu SGe YLi YChen XZhang YDemartini GZuccon GCulpepper JHuang ZTong H(2021)Counterfactual Explainable RecommendationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482420(1784-1793)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482420

Index Terms

CSR 2021: The 1st International Workshop on Causality in Search and Recommendation
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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

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Cited By

Ruiz De villa ASottocornola GCoba LLucchesi FSkorulski B(2024)Ranking the causal impact of recommendations under collider bias in k-spots recommender systemsACM Transactions on Recommender Systems10.1145/36431392:2(1-29)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3643139
Tan JXu SGe YLi YChen XZhang YDemartini GZuccon GCulpepper JHuang ZTong H(2021)Counterfactual Explainable RecommendationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482420(1784-1793)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482420

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