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dc.contributor.authorLumbreras, Alberto
dc.contributor.authorGavaldà Mestre, Ricard
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2016-11-02T08:20:23Z
dc.date.available2016-11-02T08:20:23Z
dc.date.issued2012
dc.identifier.citationLumbreras, A., Gavaldà, R. "Applying trust metrics based on user interactions to recommendation in social networks". 2012.
dc.identifier.urihttp://hdl.handle.net/2117/91313
dc.description.abstractRecommender systems have been strongly researched within the last decade. With the arising and popularization of digital social networks a new field has been opened for social recommendations. Considering the network topology, users interactions, or estimating trust between users are some of the new strategies that recommender systems can take into account in order to adapt their techniques to these new scenarios. We introduce MarkovTrust, a way to infer trust from Twitter interactions and to compute trust between distant users. MarkovTrust is based on Markov chains, which makes it simple to be implemented and computationally efficient. We study the properties of this trust metric and study its application in a recommender system of tweets.
dc.format.extent8 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-12-9-R
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.otherSocial networks
dc.subject.otherTrust
dc.subject.otherRecommendation
dc.subject.otherData mining
dc.subject.otherMachine learning
dc.titleApplying trust metrics based on user interactions to recommendation in social networks
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.rights.accessOpen Access
local.identifier.drac19241013
dc.description.versionPostprint (published version)
local.citation.authorLumbreras, A.; Gavaldà, R.


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