Constraint-based recommender systems: technologies and research issues

  • Authors:
  • A. Felfernig;R. Burke

  • Affiliations:
  • University of Klagenfurt, Austria;DePaul University Chicago, IL

  • Venue:
  • Proceedings of the 10th international conference on Electronic commerce
  • Year:
  • 2008

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Abstract

Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid-1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.