Trust and nuanced profile similarity in online social networks

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. (2017, neutral): “These characteristics can be mined from the users’ profiles which include the information related to interests, preferences, and demographic [12].”
Recommender Systems (2016, neutral): “The strong correlation between trust and homophily has been shown in [224, 681].”
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.”
Exploiting homophily effect for trust prediction (2013, background): “from whom we should accept information and with whom we should share information [6], plays an important role in helping online users collect reliable information.”
A model of a trust-based recommendation system on a social network (2006, background): “This is consistent with the observation in the literature that there is a correlation between trust and similarity [16] and that, in a recommendation system, “recommendations only make sense when obtained from like-minded people exhibiting similar taste” [34].”