Information Retrieval Journal

Aims & Scope

The journal provides an international forum for the publication of theory, algorithms, analysis and experiments across the broad area of information retrieval.

Topics of interest include search, indexing, analysis, and evaluation for applications such as the web, social and streaming media, recommender systems, and text archives.

This includes research in areas such as human factors, artificial intelligence, and domain-specific challenges in information retrieval.

Metrics & Ranking

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

Subjects & Keywords

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

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.

• An Evaluation of Statistical Approaches to Text Categorization

  Citation: 1118

  Authors: Yiming

• Eigentaste: A Constant Time Collaborative Filtering Algorithm

  Citation: 998

  Authors: Ken, Theresa, Dhruv, Chris

• Automating the Construction of Internet Portals with Machine Learning

  Citation: 841

  Authors: Andrew Kachites, Kamal, Jason, Kristie

• A comparison of extrinsic clustering evaluation metrics based on formal constraints

  Citation: 525

  Authors: Enrique, Julio, Javier, Felisa

• Learning Algorithms for Keyphrase Extraction

  Citation: 501

  Authors: Peter D.

• An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms

  Citation: 474

  Authors: Jon, Joseph A., John

• Features for image retrieval: an experimental comparison

  Citation: 382

  Authors: Thomas, Daniel, Hermann

• Adapting boosting for information retrieval measures

  Citation: 364

  Authors: Qiang, Christopher J. C., Krysta M., Jianfeng

• LETOR: A benchmark collection for research on learning to rank for information retrieval

  Citation: 316

  Authors: Tao, Tie-Yan, Jun, Hang

• A machine learning approach to sentiment analysis in multilingual Web texts

  Citation: 311

  Authors: Erik, Marie-Francine