July 15, 2021, Online

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

Co-located with The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

About

The motivation of the workshop is to promote the research and application of Causal Analysis and Causal Modeling in Information Retrieval tasks, including but not limited to Search, Recommendation, QA, and Dialog. Causality in IR attempts to develop causal models not only to improve the ranking performance but also benefit IR systems in a broader scope of perspectives such as explainability, fairness, robustness, trustworthiness, etc.

In a broader sense, researchers in the broader AI community have also realized the importance of advancing from correlative learning to causal learning, which aims to address a wide range of AI problems in machine learning, machine reasoning, computer vision, autonomous systems, and natural language processing tasks. As an important branch of AI research, it highlights the importance our IR/RecSys communities to advance from correlative modeling to causal modeling in various search, recommendation, QA and dialog systems.

We welcome contributions of both technical and perspective papers. Paper should be at least 4 pages and at most 12 pages using standard ACM double column template, any page number in between 4 and 12 are welcome. Space for references is unlimited. We welcome papers from a wide range of topics, including but not limited to causal search and recommendation models, incorporating multi-modal information for causal modeling, evaluation of causal search and recommendation models, user study of causal models, as well as causal models for explainable, fair, unbiased, and robust IR. More topics are listed in the call for papers. Papers must be submitted to EasyChair by 23:59, AoE (Anywhere on Earth) on Apr 29 (Abstract) and May 6 (Fullpaper), 2021. Notifications will be sent on May 20, 2021.

CSR'21 (co-located with SIGIR'21)
Online
Sponsers

Call for Papers

We welcome contributions of both technical and perspective papers from a wide range of topics, including but not limited to the following topics of interest:

  1. New Models for Causal IR
    • Interventional learning/reasoning models
    • Counterfactual learning/reasoning models
    • Causal mining models
    • Sequential causal modeling
    • Novel causal priors for IR
    • Novel causal structures for IR
  2. Theoretical Guarantees for Causal Models
    • Theory for treatment-effect estimation
    • Theory for causal structural models
    • Theory for unmeasured confounder
    • Identifiability of causal models
    • Theory on causal graph discovery
  3. Causal models for Explainable IR
    • Causal explainable search
    • Causal explainable recommendation
    • Causal explainable question answering
    • Causal explainable conversational systems
    • Counterfactual explanations
    • Causal evaluation of explanations
    • Causal multimodal explanations
  4. Causal models for Unbiased IR
    • Causal data debias
    • Causal model debias
    • Causal debias in search
    • Causal debias in recommendation
    • Feedback loops and echo chambers
  5. Causal models for Fairness in IR
    • Counterfactual fairness in search
    • Counterfactual fairness in recommendation
    • Causal evaluation of fairness
    • Fairness-utility trade-off
  6. Causal models for Robust IR
    • Causal anti-spam models
    • Causal shilling attack detection
    • Causal data imputation
    • Counterfactual fake detection
    • Causal sensitivity analysis of IR models
  7. Multi-modality Causal Learning
    • Text-based causal learning
    • Image-based causal learning
    • Knowledge-enhanced causal learning
    • Audio/video-based causal learning
    • Causal learning with heterogeneous information
  8. User Behavior and Causal Models
    • User interaction with causal models
    • Causal attribution of user behaviors
    • Causal click models
    • Causal assumption of user behaviors
  9. Evaluation of Causal Models
    • Treatment effect of causal models
    • Interventional evaluation
    • Counterfactual evaluation
    • User study for causal evaluation
    • New datasets for causal evaluation
  10. Causal Modeling for Different Applications
    • Causal modeling in search engine
    • Causal modeling in recommender systems
    • Causal modeling in e-commerce
    • Causal modeling in social networks
    • Causal modeling in QA systems
    • Causal modeling in conversational systems

PAPER SUBMISSION GUIDLINES

CSR 2021 paper submissions should be at least 4 pages and at most 12 pages using standard double-column ACM SIG proceedings format, any page number in between 4 and 12 are welcome. Space for references is unlimited. Each accepted paper will have an oral presentation in a plenary session, and will also be allocated a presentation slot in a poster session to encourage discussion and follow up between authors and attendees.

CSR 2021 submissions are double-blind. All submissions and reviews will be handled electronically. Additional information about formatting and style files is available on the ACM website. Papers must be submitted to easychair at https://easychair.org/conferences/?conf=csr20210 by 23:59, AoE (Anywhere on Earth) on April 29 (Abstract) and May 6 (Fullpaper), 2021.

For inquires about the workshop and submissions, please email to csr2021-0@easychair.org

Important Days

All time are 23:59, AoE (Anywhere on Earth)
Apr 29, 2021: Abstract due
May 6, 2021: Submission due
May 20, 2021: Paper notification
June 20, 2021: Camera ready submission
July 15, 2021: Workshop day

Workshop Co-Chairs

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Yongfeng Zhang Rutgers University

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Xu Chen Renmin Univ. of China

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Yi Zhang UC Santa Cruz

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Xianjie Chen Facebook Research

THE VENUE

CSR'21 will be co-located with The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval on July 15, 2021.