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

In: Computing and Informatics, vol. 28, no. 4

T. Horváth

Detaily:

Rok, strany: 2009, 453 - 481

Kľúčové slová:

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

O článku:

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.

Ako citovať:

ISO 690:

Horváth, T. 2009. A Model of User Preference Learning for Content-Based Recommender Systems. In Computing and Informatics, vol. 28, no.4, pp. 453-481. 1335-9150.

APA:

Horváth, T. (2009). A Model of User Preference Learning for Content-Based Recommender Systems. Computing and Informatics, 28(4), 453-481. 1335-9150.