Using Trust and Provenance for Content Filtering on the Semantic Web

Social networks are a popular movement on the web. Trust can be used eectively on the Semantic Web as annotations to social relationships. In this paper, we present a two level approach to integrating trust, provenance, and annotations in Semantic Web systems. We describe an algorithm for inferring trust relationships using provenance information and trust annotations in Semantic Web-based social networks. Then, we present two applications that combine the computed trust values with the provenance of other annotations to personalize websites. The FilmTrust system uses trust to compute personalized recommended movie ratings and to order reviews. An open source intelligence portal, Profiles In Terror, also has a beta system that integrates social networks with trust annotations. We believe that these two systems illustrate a unique way of using trust annotations and provenance to process information on the Semantic Web.

[1]  Hector Garcia-Molina,et al.  The Eigentrust algorithm for reputation management in P2P networks , 2003, WWW '03.

[2]  Kirsten Swearingen,et al.  Beyond Algorithms: An HCI Perspective on Recommender Systems , 2001 .

[3]  J. Golbeck,et al.  FilmTrust: movie recommendations using trust in web-based social networks , 2006, CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006..

[4]  Jennifer Golbeck,et al.  Investigating interactions of trust and interest similarity , 2007, Decis. Support Syst..

[5]  Cai-Nicolas Ziegler.  Investigating Correlations of Trust and Interest Similarity-Do Birds of a Feather Really Flock Together ? , 2005 .

[6]  Jennifer Golbeck,et al.  Computing and Applying Trust in Web-based Social Networks , 2005 .

[7]  Jennifer Golbeck,et al.  Generating Predictive Movie Recommendations from Trust in Social Networks , 2006, iTrust.

[8]  Rashmi R. Sinha,et al.  Comparing Recommendations Made by Online Systems and Friends , 2001, DELOS.

[9]  Torsten Schaub,et al.  Expressing preferences in default logic , 2000, Artif. Intell..

[10]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[11]  Thomas Beth,et al.  Valuation of Trust in Open Networks , 1994, ESORICS.

[12]  Alexander Aiken,et al.  Attack-Resistant Trust Metrics for Public Key Certification , 1998, USENIX Security Symposium.

[13]  Georg Lausen,et al.  Spreading activation models for trust propagation , 2004, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004.