Exploiting different users' interactions for profiles enrichment in recommender systems

da Costa, Arthur F., Martins, Rafael D., Manzato, Marcelo G., and Campello, Ricardo J.G.B. (2016) Exploiting different users' interactions for profiles enrichment in recommender systems. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing. pp. 1080-1082. From: SAC 2016: 31ST Annual ACM Symposium on Applied Computing, 4-8 April 2016, Pisa, Italy.

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Abstract

User profiling is an important aspect of recommender systems. It models users' preferences and is used to assess an item's relevance to a particular user. In this paper we propose a profiling approach which describes and enriches the users' preferences using multiple types of interactions. We show in our experiments that the enriched version of users' profiles is able to provide better recommendations.

Item ID: 46779
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4503-3739-7
Keywords: recommender system; user profiling; multiple interactions
Funders: FAPESP Brazil, CNPq, Brazil
Projects and Grants: FAPESP grant 2013/18698-4, FAPESP grant 2013/22547-1, CNPq 304137/2013-8
Date Deposited: 21 Mar 2017 23:50
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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