Generating Predictive Movie Recommendations from Trust in Social Networks Export

@incollection{citeulike:1869123, abstract = {Social networks are growing in number and size, with hundreds of millions of user accounts among them. One added benefit of these networks is that they allow users to encode more information about their relationships than just stating who they know. In this work, we are particularly interested in trust relationships, and how they can be used in designing interfaces. In this paper, we present FilmTrust, a website that uses trust in web-based social networks to create predictive movie recommendations. Using the FilmTrust system as a foundation, we show that these recommendations are more accurate than other techniques when the user's opinions about a film are divergent from the average. We discuss this technique both as an application of social network analysis, as well as how it suggests other analyses that can be performed to help improve collaborative filtering algorithms of all types.}, author = {Golbeck, Jennifer}, booktitle = {Trust Management}, citeulike-article-id = {1869123}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11755593_8}, citeulike-linkout-1 = {http://www.springerlink.com/content/kl313421w6252620}, doi = {10.1007/11755593_8}, journal = {Trust Management}, keywords = {analysis, annotation, multimedia, rdf, recomendaciones, recommendation, redessociales, semanticdata, semanticweb, social-networks, socialnetworks, socialweb, video}, pages = {93--104}, posted-at = {2009-03-02 19:46:39}, priority = {2}, title = {Generating Predictive Movie Recommendations from Trust in Social Networks}, url = {http://dx.doi.org/10.1007/11755593_8}, year = {2006} }

TY - CHAP ID - citeulike:1869123 L3 - citeulike-article-id:1869123 N2 - Social networks are growing in number and size, with hundreds of millions of user accounts among them. One added benefit of these networks is that they allow users to encode more information about their relationships than just stating who they know. In this work, we are particularly interested in trust relationships, and how they can be used in designing interfaces. In this paper, we present FilmTrust, a website that uses trust in web-based social networks to create predictive movie recommendations. Using the FilmTrust system as a foundation, we show that these recommendations are more accurate than other techniques when the user’s opinions about a film are divergent from the average. We discuss this technique both as an application of social network analysis, as well as how it suggests other analyses that can be performed to help improve collaborative filtering algorithms of all types. JF - Trust Management EP - 104 TI - Generating Predictive Movie Recommendations from Trust in Social Networks T2 - Trust Management SP - 93 KW - analysis KW - annotation KW - multimedia KW - rdf KW - recomendaciones KW - recommendation KW - redessociales KW - semanticdata KW - semanticweb KW - social-networks KW - socialnetworks KW - socialweb KW - video AU - Golbeck, Jennifer PY - 2006/// UR - http://dx.doi.org/10.1007/11755593_8 ER -