Robin Burke
Yong Zheng
ABSTRACT
The aim of the Experience Discovery project is to recommend extracurricular activities to high school and middle school students in urban areas. In implementing this system, we have been able to make use of both usage data and data drawn from a social networking site. Using pilot data, we are able to show that very simple aggregation techniques applied to the social network can improve recommendation accuracy.
- R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331--370, 2002. Google Scholar
Digital Library
- R. Burke. Evaluating the dynamic properties of recommendation algorithms. In Proceedings of the 4th ACM International Conference on Recommender Systems, pages 225--228, Barcelona, Spain, 2010. Google ScholarDigital Library
- J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An Algorithmic Framework for Performing Collaborative Filtering. In 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, California, 1999. ACM. Google ScholarDigital Library
- J. Herlocker, J. Konstan, L. G. Tervin, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. on Information Systems, 22(1):5--53, 2004. Google ScholarDigital Library
- M. Itō. Hanging out, messing around, and geeking out: kids living and learning with new media. MacArthur Foundation series on digital media and learning. MIT Press, 2010.Google Scholar
Index Terms
- Experience Discovery: hybrid recommendation of student activities using social network data
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