dc.contributor
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.contributor
Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.contributor.author
Lumbreras, Alberto
dc.contributor.author
Gavaldà Mestre, Ricard
dc.identifier
Lumbreras, A., Gavaldà, R. "Applying trust metrics based on user interactions to recommendation in social networks". 2012.
dc.identifier
https://hdl.handle.net/2117/91313
dc.description.abstract
Recommender 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.description.abstract
Postprint (published version)
dc.format
application/pdf
dc.subject
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject
Social networks
dc.subject
Recommendation
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Machine learning
dc.title
Applying trust metrics based on user interactions to recommendation in social networks
dc.type
External research report
