Trust-aware recommender systems

By Paolo Massa, Paolo Avesani. 2007

630 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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In Artificial Intelligence

In Computer Networks and Communications

In Information Systems

Citations in the corpus

How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “Alternatively, the user preferences θ (t) can represent the non-binary trust between users, which can be provided explicitly by the user [45] or learned from user behavior [13]; we use the latter in our analysis (section 4).”
A generalized taxonomy of explanations styles for traditional and social recommender systems (2012, background): “…X X X … … … … … … … … Multi-criteria (Adomavicius et al. 2010) X X X Social tagging (Vig et al. 2009; Marinho et al. 2010) X X X X X X X Trust aware (Golbeck 2005; Massa and Avesani 2007) X X X X X X X additional contextual information, such as time, location, weather, or the company of other…”
Session-Based Social Recommendation via Dynamic Graph Attention Networks (2019, background): “Modeling the influence of friends on user interests has also received attention [15, 16, 23–25].”
mTrust: discerning multi-faceted trust in a connected world (2012, background): “Assuming that users are likely to have similar preferences with their trust networks, trust networks are widely exploited in collaborative filtering [8, 1], intelligent recommender systems [17, 5], review quality prediction [15] and viral marketing [22] to improve the accuracy.”
Music recommendation and discovery in the long tail (2008, background): “In (Massa and Avesani, 2007), the authors emphasise the “web of trust” provided by every user.”