Recommendations with prerequisites

RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems

Volumn , Issue , 2009, Pages 353-356

  Parameswaran, Aditya G ^a   Garcia Molina, Hector ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Graph theory; Recommendation algorithms

Indexed keywords

APPROXIMATE ALGORITHMS; COMPLEX ANALYSIS; NP-HARD; RECOMMENDATION ALGORITHMS; SYNTHETIC DATA;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; GRAPH THEORY;

PROBLEM SOLVING;

EID: 72249101703     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1639714.1639786     Document Type: Conference Paper

References (10)

1. G. Adomavicius, A. Tuzhilin, and R. Zheng. Rql: A Query Language for Recommender Systems.
2. G. Adomavicius and E. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 17:734-749, 2005.
3. R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331-370, 2002.
4. M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst., 22(1):143-177, 2004.
5. A. Felfernig and R. Burke. Constraint-based recommender systems: technologies and research issues. In EC '08.
6. A. Parameswaran and H. Garcia-Molina. Recommendations with prerequisites. http://ilpubs.stanford.edu:8090/920/.
7. A. Parameswaran, P. Venetis, and H. Garcia-Molina. Recommendation systems with complex constraints: A courserank perspective. http://ilpubs.stanford.edu: 8090/909/.
8. M. Pazzani and D. Billsus. Content-based recommendation systems. In The Adaptive Web, pages 325-341, 2007.
9. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In WWW '01.
10. C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In WWW '05.