Exploiting different users' interactions for profiles enrichment in recommender systems

Arthur F. Da Costa, Rafael D. Martins, Marcelo G. Manzato, Ricardo J.G.B. Campello

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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.

OriginalsprogEngelsk
Titel2016 Symposium on Applied Computing, SAC 2016
ForlagAssociation for Computing Machinery
Publikationsdato4. apr. 2016
Sider1080-1082
ISBN (Elektronisk)9781450337397
DOI
StatusUdgivet - 4. apr. 2016
Udgivet eksterntJa
Begivenhed31st Annual ACM Symposium on Applied Computing, SAC 2016 - Pisa, Italien
Varighed: 4. apr. 20168. apr. 2016

Konference

Konference31st Annual ACM Symposium on Applied Computing, SAC 2016
Land/OmrådeItalien
ByPisa
Periode04/04/201608/04/2016
SponsorACM Special Interest Group on Applied Computing (SIGAPP)

Bibliografisk note

Publisher Copyright:
© 2016 ACM.

Fingeraftryk

Dyk ned i forskningsemnerne om 'Exploiting different users' interactions for profiles enrichment in recommender systems'. Sammen danner de et unikt fingeraftryk.

Citationsformater