Flexible recommendations for course planning

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2009, Pages 1467-1470

  Koutrika, Georgia ^a   Bercovitz, Benjamin ^a   Ikeda, Robert ^a   Kaliszan, Filip ^a   Liou, Henry ^a   Garcia Molina, Hector ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COURSE PLANNING; REAL SYSTEMS; RECOMMENDATION METHODS; RELATIONAL DATA; WORK-FLOWS;

TELEPHONE SYSTEMS;

EID: 67649655736     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2009.127     Document Type: Conference Paper

References (10)

1. The CourseRank system: url: http://courserank.stanford.edu/.
2. The Stanford Daily: url: http://stanforddaily.com/article/2007/12/5/- editorialcourserankalongoverduesuccess.
3. G. Adomavicius and Y. Kwon. New recommendation techniques for multi-criteria rating systems. IEEE Intelligent Systems, 22(3), 2007.
4. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Engineering, 17(6):734-749, 2005.
5. A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization:scalable online collaborative filtering. In WWW, pages 271-280,2007.
6. D. Goldberg, D. Nichols, B. Oki, and D. Terry. Using collaborative filtering to weave an information Tapestry. CACM, 35(12):61-70, 1992.
7. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. TOIS, 22:553, 2004.
8. G. Koutrika, R. Ikeda, B. Bercovitz, and H. Garcia-Molina. Flexible recommendations over rich data. In RecSys, 2008.
9. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Itemto- item collaborative filtering. IEEE Internet Computing, Jan/Feb 2003.
10. B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Towards trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM TOIT, 7(2), 2007.