Recommender systems for intelligence analysts

Anna L. Buczak, Benjamin Grooters, Paul Kogut, Eren Manavoglu, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Homeland security intelligence analysts need help finding relevant information quickly in a rapidly increasing volume of incoming raw data. Many different AI techniques are needed to handle this deluge of data. This paper describes initial investigations in the application of recommender systems to this problem. It illustrates various recommender systems technologies and suggests scenarios for how recommender systems can be applied to support an analyst. Since unclassified data on the search behavior of analysts is hard to obtain we have built a proof-of-concept demo using analogous search behavior data in the computer science domain. The proof-of-concept collaborative recommender system that we developed is described.

Original languageEnglish (US)
Title of host publicationAI Technologies for Homeland Security - Papers from the 2005 AAAI Spring Symposium, Technical Report
Number of pages6
StatePublished - 2005
Event2005 AAAI Spring Symposium - Stanford, CA, United States
Duration: Mar 21 2005Mar 23 2005


Other2005 AAAI Spring Symposium
Country/TerritoryUnited States
CityStanford, CA

All Science Journal Classification (ASJC) codes

  • General Engineering


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