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Learning User Preferences for 2CP-Regression for a Recommender System
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SYSNO ASEP 0338369 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Learning User Preferences for 2CP-Regression for a Recommender System Author(s) Eckhardt, Alan (UIVT-O)
Vojtáš, Peter (UIVT-O)Source Title SOFSEM 2010. Theory and Practice of Computer Science. - Berlin : Springer, 2010 / van Leeuwen J. ; Muscholl A. ; Peleg D. ; Pokorný J. ; Rumpe B. - ISSN 0302-9743 - ISBN 978-3-642-11265-2 Pages s. 346-357 Number of pages 12 s. Action SOFSEM 2010. Conference on Current Trends in Theory and Practice of Computer Science /36./ Event date 23.01.2010-29.01.2010 VEvent location Špindlerův Mlýn Country CZ - Czech Republic Event type WRD Language eng - English Country DE - Germany Keywords user preferences ; machine learning ; ordering Subject RIV IN - Informatics, Computer Science R&D Projects 1ET100300517 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) GD201/09/H057 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000280086900029 EID SCOPUS 77249132298 DOI 10.1007/978-3-642-11266-9_29 Annotation In this paper we deal with a task to learn a general user model from user ratings of a small set of objects. This general model is used to recommend top-k objects to the user. We consider several (also some new) alternatives of learning local preferences and several alternatives of aggregation (with or without 2CP-regression). The main contributions are evaluation of experiments on our prototype tool PrefWork with respect to several satisfaction measures and the proposal of method Peak for normalisation of numerical attributes. Our main objective is to keep the number of sample data which the user has to rate reasonable small. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2010
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