Investigating interactions of trust and interest similarity

By Cai-Nicolas Ziegler, Jennifer Golbeck. Decision Support Systems, 2007

406 citations recorded by the source corpus.

How researchers see this paper

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

Collaborative Filtering Recommender Systems (2011, background): “Ziegler and Golbeck [154] found that trust and conventional rating similarities are correlated.”
A survey of collaborative filtering based social recommender systems (2014, background): “Empirical studies in [28,29] found a correlation between trust and user similarity.”
Exploiting homophily effect for trust prediction (2013, background): “pointed out that there is a strong and significant correlation between trust and user preference similarity [27].”
mTrust: discerning multi-faceted trust in a connected world (2012, background): “pointed out that there is a strong and significant correlation between trust and similarity [25].”
From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers' purchase intention (2010, background): “For instance, in book recommender systems, people are inclined to adopt recommendations from those that are more similar to them (Ziegler and Golbeck 2007).”
Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. (2017, positive): “While, the implicit trust refers to the trust inferred from some evidence such as feature similarity among users [50] [5], their behaviors [24] [31], the age of their relationships [18] [27].”
Recommender Systems (2016, positive): “The strong correlation between trust and homophily has been shown in [224, 681].”