SIGIR 2021 : The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Conference Series : International ACM SIGIR Conference on Research and Development in Information Retrieval
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.
Time zone: Anywhere on Earth (AoE)
Full paper abstracts due: Tue, Feb 2, 2021
Full papers due: Tue, Feb 9, 2021
Full paper notifications: Mon, Apr 19, 2021
See a brief checklist to strengthen an IR paper, for authors and reviewers.
Full research papers must describe original work that has not been previously published, not accepted for publication elsewhere, and not simultaneously submitted or currently under review in another journal or conference (including the short paper track of SIGIR 2021).
Submissions of full research papers must be in English, in PDF format, and be at most 9 pages (including figures) in length + unrestricted space for references, in the current ACM two-column conference format. Suitable LaTeX, Word, and Overleaf templates are available from the ACM Website (use the“sigconf” proceedings template).
Submissions must be anonymous and should be submitted electronically via EasyChair:
At least one author of each accepted paper is required to register for, and present the work at the conference.
The CFP for other paper tracks, as well as workshops, tutorials, doctoral consortium, industry day, and other SIGIR 2021 venues will be released separately.
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 arXiv.org 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 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).
Pablo Castells, Universidad Autónoma de Madrid
Rosie Jones, Spotify
Tetsuya Sakai, Waseda University
For any questions about full paper submissions you may contact the Program Chairs by email to sigir2021-pcchairs AT easychair DOT org.