Research Output
A Model of User Preference Learning for Content-Based Recommender Systems
  This paper focuses to a formal model of user preference learning for
content-based recommender systems. First, some fundamental and special requirements to user preference learning are identified and proposed. Three learning tasks are introduced as the exact, the order preserving and the iterative user preference learning tasks. The first two tasks concern the situation where we have the user’s rating available for a large part of objects. The third task does not require any prior knowledge about the user’s ratings (i.e. the user’s rating history). Local and global preferences are distinguished in the presented model. Methods for learning these preferences are discussed. Finally, experiments and future work will be described.

  • Type:


  • Date:

    31 December 2009

  • Publication Status:


  • ISSN:


  • Funders:

    Historic Funder (pre-Worktribe)


Horvath, T. (2009). A Model of User Preference Learning for Content-Based Recommender Systems. Computing and Informatics, 28(4), 1001-1029



Content-based recommender systems, user preference learning, induction of fuzzy and annotated logic programs

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