Search personalization: Knowledge-based recommendation in digital libraries

Todd Will, Anand Srinivasan, Il Im, Yi Fang Brook Wu

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

3 Scopus citations

Abstract

Recommendation engines have made great strides in understanding and implementing search personalization techniques to provide interesting and relevant documents to users. The latest research effort advances a new type of recommendation technique, Knowledge Based (KB) engines, that strive to understand the context of the user's current information need and then filter information accordingly. The KB engine proposed in this paper requires less effort from the user in representing the search task and is the first of its kind implemented in a digital library setting. The KB engine performance was compared with Content Based (CB) and Collaborative Filtering (CF) recommendation techniques and the text search engine Lucene by asking sixty subjects to perform two different tasks to find relevant documents in a database of 212,000 documents from 22 National Science Digital Library (NSDL) collections. Our KB engine design outperforms CB, CF, and text search techniques in nearly all areas of evaluation.

Original languageEnglish (US)
Title of host publication15th Americas Conference on Information Systems 2009, AMCIS 2009
Pages6443-6450
Number of pages8
StatePublished - 2009
Event15th Americas Conference on Information Systems 2009, AMCIS 2009 - San Francisco, CA, United States
Duration: Aug 6 2009Aug 9 2009

Publication series

Name15th Americas Conference on Information Systems 2009, AMCIS 2009
Volume10

Other

Other15th Americas Conference on Information Systems 2009, AMCIS 2009
Country/TerritoryUnited States
CitySan Francisco, CA
Period8/6/098/9/09

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems
  • Library and Information Sciences

Keywords

  • Collaborative filtering
  • Content based
  • Knowledge based
  • Personalized search
  • Recommendation engines
  • Text search

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