A Model of User Preference Learning for Content-Based Recommender Systems

keywords: Content-based recommender systems, user preference learning, induction of fuzzy and annotated logic programs
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.
mathematics subject classification 2000: 68T05, 03B52, 03B80
reference: Vol. 28, 2009, No. 4, pp. 453–481