mTrust: discerning multi-faceted trust in a connected world

By Jiliang Tang, Huiji Gao, Huan Liu. Web Search and Data Mining, 2012

389 citations recorded by the source corpus.

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Exploiting homophily effect for trust prediction (2013, methodology): “LOW-RANK MATRIX FACTORIZATION MODEL FOR TRUST PREDICTION Low-rank matrix factorization method is widely employed in various applications such as collective filtering [8, 21] and document clustering [26, 2].”
Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, background): “Although the impact of the parameters of a recommendation strategy has been often inspected [9, 39, 22, 41, 29, 20, 7], little systematic effort has been devoted into understanding how various user or item attributes are affecting the performance [2], and none of such analyses, to our knowledge, hav”
Exploring temporal effects for location recommendation on location-based social networks (2013, methodology): “To solve largescale recommendation problems, matrix factorization is state-ofthe-art technology that has been proven to be successful in the Netflix Competition [11, 12], and is being used for item recommendation and trust prediction on product review sites like Epinions and Ciao for research purpos”
Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Moreover, conformity bias might be caused by social influence, where users tend to behave similarly with their friends [25], [26], [27], [28].”