A graph-based recommender system for digital library

  • Authors:

  • Zan Huang;Wingyan Chung;Thian-Huat Ong;Hsinchun Chen

  • Affiliations:

  • The University of Arizona, Tucson, AZ;The University of Arizona, Tucson, AZ;The University of Arizona, Tucson, AZ;The University of Arizona, Tucson, AZ

  • Venue:
  • Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 approach). 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.