Personalized query relaxations and repairs in knowledge-based recommendation

ABSTRACT

Knowledge-based recommender systems are applications that support users in the process of retrieving items from a complex product assortment (e.g. computers, holiday packages, and financial services). Recommendations are determined on the basis of explicitly defined user requirements which can be interpreted as constraints to be fulfilled by the items stored in a product table. If no solution (item) can be found, existing knowledge-based recommenders propose non-personalized query relaxations and repair actions for the given set of customer requirements that support a recovery from the dead-end. This paper points out how the usability of knowledge-based recommender systems can be improved by introducing the concept of personalized query relaxations and repair actions.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems

General Terms:
Algorithms, Design, Experimentation

Keywords:
model-based diagnosis, personalization, recommender systems