ACM Transactions on Information Systems

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	9.1
2024	5.40

Journal Rank

Year	Value
2024	2275

Journal Citation Indicator

Year	Value
2024	2904

SJR (SCImago Journal Rank)

Year	Value
2024	1.540

Quartile

Year	Value
2024	Q1

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Business, Management and Accounting and Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Evaluating collaborative filtering recommender systems

  Citation: 4024

  Authors: Jonathan L., Joseph A., Loren G., John T.

• Cumulated gain-based evaluation of IR techniques

  Citation: 3150

  Authors: Kalervo, Jaana

• The active badge location system

  Citation: 2309

  Authors: Roy, Andy, Veronica, Jonathan

• Item-based top-<i>N</i>recommendation algorithms

  Citation: 1670

  Authors: Mukund, George

• Technological frames

  Citation: 1053

  Authors: Wanda J., Debra C.

• Latent semantic models for collaborative filtering

  Citation: 980

  Authors: Thomas

• Measuring praise and criticism

  Citation: 888

  Authors: Peter D., Michael L.

• Incorporating contextual information in recommender systems using a multidimensional approach

  Citation: 840

  Authors: Gediminas, Ramesh, Shahana, Alexander

• A study of smoothing methods for language models applied to information retrieval

  Citation: 780

  Authors: Chengxiang, John

• Bias in computer systems

  Citation: 764

  Authors: Batya, Helen

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