FlexRecs: Expressing and combining flexible recommendations

SIGMOD-PODS'09 - Proceedings of the International Conference on Management of Data and 28th Symposium on Principles of Database Systems

Volumn , Issue , 2009, Pages 745-757

  Koutrika, Georgia ^a   Bercovitz, Benjamin ^a   Garcia Molina, Hector ^a  

^a Stanford University   (United States)

Author keywords

Flexible recommendations; Recommendation operators; Recommendation queries

Indexed keywords

PARAMETERIZED; RECOMMENDATION ENGINE; RECOMMENDATION METHODS; RECOMMENDATION SYSTEMS; RELATIONAL OPERATOR; STRUCTURED DATA; WORK-FLOWS;

COMBINES; MANAGEMENT; TELEPHONE SYSTEMS;

DATABASE SYSTEMS;

EID: 70849123244     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1559845.1559923     Document Type: Conference Paper

References (27)

1. Problems with recommendations: url: http://www.amazon.com/problemswith- kindle-storerecommendations/forum/fxbvkst06pwp9b/tx1togp7gq4xa4t/1?-encoding= utf8&asin=b000fi73ma.
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. Multidimensional recommender systems: A data warehousing approach. In WELCOM, 2001.
5. 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.
6. M. Balabanovic and Y. Shoham. Fab: Content-based, collaborative recommendation. C. of ACM, 40(3):66-72, 1997.
7. D. Billsus and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2-3):147-180, 2000.
8. J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In 14th UAI Conf., 1998.
9. L. Colby. A recursive algebra for nested relations. Inf. Syst., 15(5):567-582, 1990.
10. A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW, pages 271-280, 2007.
11. V. Deshpande and P. Larson. Transforming from flat algebra to nested algebra. System Sciences, 2(2-5):298-307, 1990.
12. D. Goldberg, D. Nichols, B. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. C. of ACM, 35(12):61-70, 1992.
13. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. TOIS, 22:5-53, 2004.
14. G. Karypis. Evaluation of item-based top-n recommendation algorithms. In CIKM, 2001.
15. G. Koutrika, R. Ikeda, B. Bercovitz, and H. Garcia-Molina. Flexible recommendations over rich data. In RecSys, pages 203-210, 2008.
16. G. Koutrika, B. Bercovitz, R. Ikeda, F. Kaliszan, H. Liou, and H. Garcia-Molina. Flexible Recommendations for Course Planning. In ICDE, 2009.
17. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, Jan/Feb 2003.
18. M. Mahony, N. Hurley, N. Kushmerick, and G. Silvestre. Collaborative recommendation: A robustness analysis. ACM TOIT, 4(4):344-377, 2004.
19. B. Miller, I. Albert, S. Lam, J. Konstan, and J. Riedl. Movielens unplugged: Experiences with an occasionally connected recommender system. In Int'l Conf. Intelligent User Interfaces, 2003.
20. B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Towards trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. On Internet Technology, 7(2), 2007.
21. M. Pazzani and D. Billsus. Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27:313-331, 1997.
22. P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Conf. on Computer Supported Cooperative Work, 1994.
23. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In 10th WWW Conf., 2001.
24. A. Schein, A. Popescul, L. Ungar, and D. Pennock. Methods and metrics for cold-start recommendations. In ACM SIGIR Conf., 2002.
25. H. J. Schek and M. H. Scholl. The relational model with relation-valued attributes. Inf. Syst., 11(2):137-147, 1986.
26. U. Shardanand and P. Maes. Social information filtering: Algorithms for automating 'word of mouth'. In Conf. on Human Factors in Comp. Sys., 1995.
27. B. Sheth and P. Maes. Evolving agents for personalized information filtering. In IEEE Conf. Artificial Intelligence for Applications, 1993.