![]() semantic search, named-entities, relation and event extraction). Conversational systems (e.g., conversational search interaction, dialog systems, spoken language interfaces, intelligent chat systems).Question answering (e.g., factoid and non-factoid question answering, interactive question answering, community-based question answering, question answering systems).deep learning for IR, embeddings, intelligent personal assistants and agents, unbiased learning). ![]() Machine Learning and Natural Language Processing for Search and Recommendation. information extraction, relation extraction, event extraction, query understanding, human-in-the-loop knowledge acquisition). Document representation and content analysis for search or recommendation (e.g., cross-lingual and multilingual search, NLP: summarization, text representation, linguistic analysis, readability, opinion mining and sentiment analysis, clustering, classification, topic models for search and recommendation).Filtering and recommendation (e.g., content-based filtering, collaborative filtering, recommender systems, recommendation algorithms, zero-query and implicit search, personalized recommendation).Research focusing on recommender systems, rich content representations and content analysis, such as: Search Recommendation & Content Analysis for Search and Recommendation. Theoretical models and foundations of information retrieval and access (e.g., new theory, fundamental concepts, theoretical analysis).Efficiency and scalability (e.g., indexing, crawling, compression, search engine architecture, distributed search, metasearch, peer-to-peer search, search in the cloud).Retrieval models and ranking (e.g., ranking algorithms, learning to rank, language models, retrieval models, combining searches, diversity, aggregated search, dealing with bias).Web search (e.g., ranking at web scale, link analysis, sponsored search, search advertising, adversarial search and spam, vertical search).Queries and query analysis (e.g., query intent, query understanding, query suggestion and prediction, query representation and reformulation, spoken queries).Research on core IR algorithmic topics, including IR at scale, such as: Any form of academic fraud or dishonesty. ![]() Content that has been determined to have been copied from other sources.Addition of authors after abstract submission.It is recommended to hold these for the final published version and submit source code for artifact review. Links to source code repositories that reveal author identities, or extended versions of the current paper.Authors or authors’ institutional affiliations clearly named or easily discoverable.Formatting not in line with the guidelines provided above.Figures, tables, proofs, appendixes, acknowledgements, or any other content after page 9 of the submission.Any of the following may result in desk rejection: Submissions that violate the preprint policy, anonymity, length, or formatting requirements, or are determined to violate ACM’s policies on academic dishonesty, including plagiarism, author misrepresentation, falsification, etc., are subject to desk rejection by the chairs. So please, make sure you have them listed correctly when submissions close. No changes to authorship will be permitted for the camera-ready submission under any circumstance or after submissions close. ![]() To support the identification of reviewers with conflicts of interest, the full author list must be specified at submission time. Please ensure that all authors are clearly identified in Eas圜hair before the submission deadline. Breaking anonymity puts the submission at risk of being desk rejected.Īuthors should carefully go through ACM’s authorship policy before submitting a paper. Do not upload the paper to a preprint site after submission to SIGIR-wait until a review decision to avoid reviewers seeing the paper in daily digests or other places. If the paper is already on arXiv, please change the title and abstract so that it is not immediately obvious they are the same. 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. However, it is acceptable to refer to companies or organizations that provided datasets, hosted experiments or deployed solutions if there is no implication that the authors are currently affiliated with these organizations. The submission must not include author information and must not include citations or discussion of related work that would make the authorship apparent. Authors are required to take all reasonable steps to preserve the anonymity of their submission. The full paper review process is double-blind.
0 Comments
Leave a Reply. |