FilmTrust: movie recommendations using trust in web-based social networks

By Jennifer Golbeck, J. Hendler. CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006., 2006

436 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.

A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (2015, background): “Others assume that trust is propagated [2] or computed from the structure of the network [10].”
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “Domains include recommenders [33], decision support [62], medical [19, 99], business processes [88, 98], e-commerce [164, 186], social networks [63, 161], smart sensors [112, 113], smart homes [2, 30, 183], games [134, 182], and public displays [179].”
Recommender Systems (2016, positive): “The Filmtrust system [225] showed how to use trust metrics for movie recommendations.”
Linked open data to support content-based recommender systems (2012, background): “Filmtrust [11] integrates Semantic Webbased social networking into a movie recommender system.”
A survey of collaborative filtering based social recommender systems (2014, background): “As to infer the trust value of user u to v , who are not directly connected , TidalTrust aggregates the trust value from u ' s direct neighbors to v , weighted by the direct trust values from u to its direct neighbors : S u ; v ¼ P w2F þ u S u ; w S w ; v P w2F þ u S u ; w : ð32Þ Once the raters hav”