Abstract
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 language | English (US) |
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Title of host publication | AI Technologies for Homeland Security - Papers from the 2005 AAAI Spring Symposium, Technical Report |
Pages | 26-31 |
Number of pages | 6 |
Volume | SS-05-01 |
State | Published - 2005 |
Event | 2005 AAAI Spring Symposium - Stanford, CA, United States Duration: Mar 21 2005 → Mar 23 2005 |
Other
Other | 2005 AAAI Spring Symposium |
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Country/Territory | United States |
City | Stanford, CA |
Period | 3/21/05 → 3/23/05 |
All Science Journal Classification (ASJC) codes
- Engineering(all)