Trust-Based Rating Prediction for Recommendation in Web 2.0 Collaborative Learning Social Software

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.


Published in:
Proceedings of the 9th International Conference on Information Technology Based Higher Education and Training, 208 - 212
Presented at:
9th International Conference on Information Technology Based Higher Education and Training, Cappadocia, Turkey, April 29 - May 1
Year:
2010
ISBN:
978-1-4244-4810-4
Keywords:
Laboratories:




 Record created 2010-04-07, last modified 2018-03-17

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