As proved by the continuous growth of the number of websites which embody recommender systems as a way of personalizing theexperience of users with their content, recommender systems representone of the most popular applications of principles and techniques com-ing from Information Filtering (IF). As IF techniques usually perform aprogressive removal of non-relevant content according to the informationstored in a user profile, recommendation algorithms process informationabout user interests - acquired in an explicit (e.g., letting users expresstheir opinion about items) or implicit (e.g., studying some behavioralfeatures) way - and exploit these data to generate a list of recommendeditems. Although each type of filtering method has its own weaknessesand strengths, preference handling is one of the core issues in the designof every recommender system: since these systems aim to guide users in apersonalized way to interesting or useful objects in a large space of possi-ble options, it is important for them to accurately catch and model userpreferences. The paper provides a general overview of the approaches tolearning preference models in the context of recommender systems

Preference Learning in Recommender Systems / De Gemmis, Marco; Iaquinta, Leo; Lops, Pasquale; Musto, Cataldo; Narducci, Fedelucio; Semeraro, Giovanni. - ELETTRONICO. - (2009), pp. 41-55. (Intervento presentato al convegno The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML/PKDD 2009 : Workshop on Preference Learning tenutosi a Bled, Slovenia nel September 11, 2009).

Preference Learning in Recommender Systems

Narducci, Fedelucio;
2009-01-01

Abstract

As proved by the continuous growth of the number of websites which embody recommender systems as a way of personalizing theexperience of users with their content, recommender systems representone of the most popular applications of principles and techniques com-ing from Information Filtering (IF). As IF techniques usually perform aprogressive removal of non-relevant content according to the informationstored in a user profile, recommendation algorithms process informationabout user interests - acquired in an explicit (e.g., letting users expresstheir opinion about items) or implicit (e.g., studying some behavioralfeatures) way - and exploit these data to generate a list of recommendeditems. Although each type of filtering method has its own weaknessesand strengths, preference handling is one of the core issues in the designof every recommender system: since these systems aim to guide users in apersonalized way to interesting or useful objects in a large space of possi-ble options, it is important for them to accurately catch and model userpreferences. The paper provides a general overview of the approaches tolearning preference models in the context of recommender systems
2009
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML/PKDD 2009 : Workshop on Preference Learning
Preference Learning in Recommender Systems / De Gemmis, Marco; Iaquinta, Leo; Lops, Pasquale; Musto, Cataldo; Narducci, Fedelucio; Semeraro, Giovanni. - ELETTRONICO. - (2009), pp. 41-55. (Intervento presentato al convegno The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML/PKDD 2009 : Workshop on Preference Learning tenutosi a Bled, Slovenia nel September 11, 2009).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/215950
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