Conference Information
SIGIR 2021: International Conference on Research and Development in Information Retrieval
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Conference Date:
Montreal, Quebec, Canada
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Call For Papers
The annual SIGIR conference is the major international forum for the presentation of new research results, and the demonstration of new systems and techniques, in the broad field of information retrieval (IR). The 44th ACM SIGIR conference, to be held in Montréal, Canada (with support for remote attendance) on July 11 to 15, 2021, welcomes contributions related to any aspect of information retrieval and access, including theories, foundations, algorithms, evaluation, analysis and applications. The conference and program chairs invite those working in areas related to IR to submit high-impact original papers for review.


The full paper review process is double-blind. Authors are required to take all reasonable steps to preserve the anonymity of their submission. The submission must not include author information and must not include citations or discussion of related work that would make the authorship apparent. Note that it is acceptable to explicitly refer in the paper to the companies or organizations that provided datasets, hosted experiments, or deployed solutions. For example, instead of stating that an experiment “was conducted on the logs of a major search engine”, the authors should refer to the search engine by name. The reviewers will be informed that it does not necessarily imply that the authors are currently affiliated with the mentioned organization. While authors can upload to institutional or other preprint repositories such as before reviewing is complete, we generally discourage this since it places anonymity at risk (which could result in a negative outcome of the reviewing process). Authors should carefully go through ACM’s authorship policy before submitting a paper. Submissions that violate the preprint policy, anonymity, length, or formatting requirements or are plagiarized are subject to desk-rejection by the chairs.

To support the identification of reviewers with conflicts of interest, the full author list must be specified at submission time. Authors should note that changes to the author list after the submission deadline are not allowed without permission from the PC Chairs.

Relevant Topics

Relevant topics include, but are not limited to the following.

Search and ranking. Research on core IR algorithmic topics, including IR at scale, such as:

    Queries and query analysis (e.g., query intent, query understanding, query suggestion and prediction, query representation and reformulation, spoken queries).
    Web search (e.g., ranking at web scale, link analysis, sponsored search, search advertising, adversarial search and spam, vertical search).
    Retrieval models and ranking (e.g., ranking algorithms, learning to rank, language models, retrieval models, combining searches, diversity, aggregated search, dealing with bias).
    Efficiency and scalability (e.g., indexing, crawling, compression, search engine architecture, distributed search, metasearch, peer-to-peer search, search in the cloud).

Foundations and theory of IR. Research with theoretical or empirical contributions on technical or social aspects of IR, such as:

    Theoretical models and foundations of information retrieval and access (e.g., new theory, fundamental concepts, theoretical analysis).
    Ethics, economics, and politics (e.g., studies on broader implications, norms and ethics, economic value, political impact, social good).
    Fairness, accountability, transparency (e.g. confidentiality, representativeness, discrimination and harmful bias).

Domain-specific applications. Research focusing on domain-specific IR challenges, such as:

    Local and mobile search (e.g., location-based search, mobile usage understanding, mobile result presentation, audio and touch interfaces, geographic search, location context in search).
    Social search (e.g., social networks in search, social media in search, blog and microblog search, forum search).
    Search in structured data (e.g., XML search, graph search, ranking in databases, desktop search, email search, entity-oriented search).
    Multimedia search (e.g., image search, video search, speech and audio search, music search).
    Education (e.g., search for educational support, peer matching, info seeking in online courses).
    Legal (e.g., e-discovery, patents, other applications in law).
    Health (e.g., medical, genomics, bioinformatics, other applications in health).
    Knowledge graph applications (e.g. conversational search, semantic search, entity search, KB question answering, knowledge-guided NLP, search and recommendation).
    Other applications and domains (e.g., digital libraries, enterprise, expert search, news search, app search, archival search, new retrieval problems including applications of search technology for social good).

Content recommendation, analysis and classification. Research focusing on recommender systems, rich content representations and content analysis, such as:

    Filtering and recommendation (e.g., content-based filtering, collaborative filtering, recommender systems, recommendation algorithms, zero-query and implicit search, personalized recommendation).
    Document representation and content analysis (e.g., summarization, text representation, linguistic analysis, readability, NLP for search, cross-lingual and multilingual search, information extraction, opinion mining and sentiment analysis, clustering, classification, topic models).
    Knowledge acquisition (e.g. information extraction, relation extraction, event extraction, query understanding, human-in-the-loop knowledge acquisition)

Artificial Intelligence, semantics, and dialog. Research bridging AI and IR, especially toward deep semantics and dialog with intelligent agents, such as:

    Core AI (e.g. deep learning for IR, embeddings, intelligent personal assistants and agents, unbiased learning).
    Question answering (e.g., factoid and non-factoid question answering, interactive question answering, community-based question answering, question answering systems).
    Conversational systems (e.g., conversational search interaction, dialog systems, spoken language interfaces, intelligent chat systems).
    Explicit semantics (e.g. semantic search, named-entities, relation and event extraction).
    Knowledge representation and reasoning (e.g., link prediction, knowledge graph completion, query understanding, knowledge-guided query and document representation, ontology modeling).

Human factors and interfaces. Research into user-centric aspects of IR including user interfaces, behavior modeling, privacy, interactive systems, such as:

    Mining and modeling users (e.g., user and task models, click models, log analysis, behavioral analysis, modeling and simulation of information interaction, attention modeling).
    Interactive search (e.g., search interfaces, information access, exploratory search, search context, whole-session support, proactive search, personalized search).
    Social search (e.g., social media search, social tagging, crowdsourcing).
    Collaborative search (e.g., human-in-the-loop, knowledge acquisition).
    Information security (e.g., privacy, surveillance, censorship, encryption, security).

Evaluation. Research that focuses on the measurement and evaluation of IR systems, such as:

    User-centered evaluation (e.g., user experience and performance, user engagement, search task design).
    System-centered evaluation (e.g., evaluation metrics, test collections, experimental design).
    Beyond Cranfield (e.g., online evaluation, task-based, session-based, multi-turn, interactive search).
    Beyond labels (e.g., simulation, implicit signals, eye-tracking and physiological signals).
    Beyond effectiveness (e.g., value, utility, usefulness, diversity, novelty, urgency, freshness, credibility, authority).
    Methodology (e.g., statistical methods, reproducibility, dealing with bias, new experimental approaches).

Pilot Track: Perspective Papers

We solicit full papers discussing an open problem in information retrieval research. We define an open problem as an area of research which deserves attention by the SIGIR community.  Open problems may be current (e.g. an overlooked problem in currently researched or deployed systems) or speculative (e.g. an unanticipated problem in future researched or deployed systems). Submissions will be evaluated according to relevance to the SIGIR community, depth and breadth of impact of the open problem, and novelty of the open problem.  

Submissions do not require new empirical results, unlike traditional SIGIR submissions. However, submissions must present new open problems and support their position with a strong base in relevant literature. Although not required, authors may include new empirical results if it supports the position.
Last updated by Tongya Zheng in 2021-03-22
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