Trust-based rating prediction for recommendation in web 2.0 collaborative learning social software

Authors:
Na Li;Sandy El Helou;Denis Gillet
Affiliations:
Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland;Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland;Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland
Venue:
ITHET'10 Proceedings of the 9th international conference on Information technology based higher education and training
Year:
2010

Citing 6
Cited 1

Trust-aware recommender systems

Proceedings of the 2007 ACM conference on Recommender systems
Community gravity: measuring bidirectional effects by trust and rating on online social networks

Proceedings of the 18th international conference on World wide web
Trust Computing for Social Networking

ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
FeedbackTrust: using feedback effects in trust-based recommendation systems

Proceedings of the third ACM conference on Recommender systems
The 3A Interaction Model: Towards Bridging the Gap between Formal and Informal Learning

ACHI '10 Proceedings of the 2010 Third International Conference on Advances in Computer-Human Interactions
Generating predictive movie recommendations from trust in social networks

iTrust'06 Proceedings of the 4th international conference on Trust Management

Trust-aware privacy control for social media

CHI '11 Extended Abstracts on Human Factors in Computing Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Benefiting from the advent of social software, information sharing becomes pervasive. Personalized rating systems have emerged to evaluate the quality of user-generated content in open environment and provide recommendation based on users' past experience. In this paper, a trust-based rating prediction approach for recommendation in Web 2.0 collaborative learning social software is proposed. Trust network is exploited in the rating prediction scheme and a multirelational trust metric is developed in an implicit way. Finally the evaluation of the approach is performed using the dataset of collaborative learning social software, namely Remashed.