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ACM Transactions on Information Systems
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The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain:

  • new principled information retrieval models or algorithms with sound empirical validation;
  • observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking;
  • accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques;
  • formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks;
  • development of content (text, image, speech, video, etc.) analysis methods to support information retrieval and information seeking;
  • development of computational models of user information preferences and interaction behaviors;
  • creation and analysis of evaluation methodologies for information retrieval and information seeking; or
  • surveys of existing work that propose a significant synthesis.
The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues' work.

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ACM Updates Its Peer Review Policy

ACM is pleased to announce that its Publications Board has approved an updated Peer Review Policy. If you have any questions regarding the update, the associated FAQ addresses topics such as confidentiality, the use of large language models in the peer review process, conflicts of interest, and several other relevant concerns. If there are any issues that are not addressed in the FAQ, please contact ACM’s Director of Publications, Scott Delman.

New ACM Policy on Authorship

ACM has a new Policy on Authorship, covering a range of key topics, including the use of generative AI tools.  Please familiarize yourself with the new policy and the associated list of Frequently Asked Questions.

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