A graph-based recommender system for digital library

Zan Huang, Wingyan Chung, Thian Huat Ong, Hsinchun Chen

Research output: Contribution to conferencePaperpeer-review

147 Scopus citations


Research shows that recommendations comprise a valuable service for users of a digital library [11]. While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approaches). In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches. Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, user-user and book-user associations. Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches. However, no significant improvement was observed by exploiting high-degree associations.

Original languageEnglish (US)
Number of pages9
StatePublished - 2002
EventProceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries - Portland, OR, United States
Duration: Jul 14 2002Jul 18 2002


OtherProceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries
Country/TerritoryUnited States
CityPortland, OR


  • Chinese phrase extraction
  • Collaborative filtering
  • Content-based filtering
  • Graph-based model
  • Hopfield net algorithm
  • Mutual information algorithm
  • Recommender system

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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