Publication
Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software
Résumé
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 multi-relational 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.
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