2026-04-13T05:44:23Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/913132025-07-17T11:59:02Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Lumbreras, Alberto author Gavaldà Mestre, Ricard author 2012 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. Postprint (published version) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Social networks Trust Recommendation Data mining Machine learning Applying trust metrics based on user interactions to recommendation in social networks