Using external aggregate ratings for improving individual recommendations

ACM Transactions on the Web

Volumn 5, Issue 1, 2011, Pages

  Umyarov, Akhmed ^a   Tuzhilin, Alexander ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

Aggregate ratings; Cold start problem; Collaborative filtering; Hierarchical linearmodels; Predictive models; Recommender systems

Indexed keywords

COLD START PROBLEMS; COLLABORATIVE FILTERING; DATA SETS; HIERARCHICAL LINEARMODELS; LARGE DATASETS; LINEAR REGRESSION MODELS; PREDICTIVE MODELS; RATING INFORMATION; RATING MODEL; SCALABLE METHODS;

AGGREGATES; DISTRIBUTED COMPUTER SYSTEMS; HIERARCHICAL SYSTEMS; LINEAR REGRESSION; PREDICTIVE CONTROL SYSTEMS; RATING; SCALABILITY; SIGNAL FILTERING AND PREDICTION;

RECOMMENDER SYSTEMS;

EID: 79951873230     PISSN: 15591131     EISSN: 1559114X     Source Type: Journal    
DOI: 10.1145/1921591.1921594     Document Type: Article

References (39)

1. ADOMAVICIUS, G., SANKARANARAYANAN, R., SEN, S., AND TUZHILIN, A. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Syst. 23, 1, 103-145. (Pubitemid 40245827)
2. ADOMAVICIUS, G. AND TUZHILIN, A. 2001. Multidimensional recommender systems: a data warehousing approach. Lecture Notes in Computer Science, 180-192. (Pubitemid 33364080)
3. ADOMAVICIUS, G. AND TUZHILIN, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Engine. 17, 6, 734-749. (Pubitemid 40860454)
4. AGARWAL, D., BRODER, A., CHAKRABARTI, D., DIKLIC, D., JOSIFOVSKI, V., AND SAYYADIAN, M. 2007. Estimating rates of rare events at multiple resolutions. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM New York, NY, 16-25. (Pubitemid 350229189)
5. ANGIESLIST. 2009. http://www.angieslist.com.
6. ANSARI, A., ESSEGAIER, S., AND KOHLI, R. 2000. Internet recommendation systems. J. Market. Resear. 37, 3, 363-375.
7. BELL, R., KOREN, Y., AND VOLINSKY, C. 2007. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM New York, NY, 95-104. (Pubitemid 350229196)
8. BENNETT, J. AND LANNING, S. 2007. The netflix prize. In Proceedings of KDD Cup and Workshop.
9. BHATIA, R. 2007. Positive Definite Matrices. Princeton University Press.
10. BISHOP, C. 2006. Pattern Recognition and Machine Learning. Springer New York.
11. BLACK, F. AND SCHOLES, M. 1973. The pricing of options and corporate liabilities. J. Polit. Econ. 81, 3, 637-654.
12. BOLLEN, J. 2000. Group usermodels for personalized hyperlink recommendations. Lecture Notes in Computer Science, 38-50.
13. BRYK, A. AND RAUDENBUSH, S. 1992. Hierarchical Linear Models: Applications and Data Analysis Methods. Sage Publications.
14. CONDLIFF, M. K., LEWIS, D. D., ANDMADIGAN, D. 1999. Bayesian mixed-effectsmodels for recommender systems. In Proceedings of the Workshop on Recommender: Algorithms and Evaluation (ACM SIGIR'99).
15. FLETCHER, R. 1987. Practical Methods of Optimization. Wiley-Interscience New York, NY.
16. FLURY, B. 1997. A First Course in Multivariate Statistics. Springer.
17. GELMAN, A. 2004. Bayesian Data Analysis. CRC Press.
18. GREENE, W. 2003. Econometric Analysis. Prentice Hall Upper Saddle River, NJ.
19. ḦARDLE, W. 2004. Nonparametric and Semiparametric Models. Springer.
20. HOX, J. 2002. Multilevel Analysis: Techniques and Applications. Lawrence Erlbaum.
21. IMDB. 2006. http://www.imdb.com.
22. JAMESON, A. AND SMYTH, B. 2007. Recommendation to groups. In The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl Eds., Springer, 596-627. (Pubitemid 351189212)
23. KARATZAS, I. AND SHREVE, S. 1991. Brownian Motion and Stochastic Calculus. Springer.
24. LINDEN, G., SMITH, B., AND YORK, J. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Intern. Comput. 7, 1, 76-80.
25. MASTHOFF, J. 2003. Modeling the multiple people that are me. Lecture notes in computer science, 258-262. (Pubitemid 36768573)
26. MASTHOFF, J. 2004. Group modeling: Selecting a sequence of television items to suit a group of viewers. User Model. User-Adapt. Interac. 14, 1, 37-85.
27. MCCARTHY, K., SALAMÓ, M., COYLE, L., MCGINTY, L., SMYTH, B., AND NIXON, P. 2006. Group recommender systems: a critiquing based approach. In Proceedings of the 11th International Conference on Intelligent User Interfaces. ACM New York, NY, 267-269. (Pubitemid 44074879)
28. MELVILLE, P., MOONEY, R., AND NAGARAJAN, R. 2002. Content-boosted collaborative filtering for improved recommendations. In Proceedings of the National Conference on Artificial Intelligence. AAAI Press, 187-192.
29. MOVIELENS. 2006. http://www.grouplens.org/node/73 (as provided in 2006).
30. NEUMAIER, A. AND GROENEVELD, E. 1998. Restricted maximum likelihood estimation of covariances in sparse linear models. Genet. Select. Evol. 30, 1, 3-26. (Pubitemid 28169219)
31. O'CONNOR, M., COSLEY, D., KONSTAN, J., AND RIEDL, J. 2001. PolyLens: A recommender system for groups of users. In Proceedings of the European Conference on Computer-Supported Cooperative Work. 199-218.
32. PARK, Y. AND TUZHILIN, A. 2008. The long tail of recommender systems and how to leverage it. In Proceedings of the ACM Conference on Recommender Systems. ACM New York, NY, 11-18.
33. SARWAR, B., KARYPIS, G., KONSTAN, J., AND REIDL, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. ACM New York, NY, 285-295.
34. SCHAFER, J., KONSTAN, J., AND RIEDL, J. 2001. E-commerce recommendation applications. Data Mini. Knowle. Discov. 5, 1, 115-153.
35. SCHEIN, A., POPESCUL, A., UNGAR, L., AND PENNOCK, D. 2002. Methods and metrics for cold-start recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM New York, NY, 253-260.
36. SCHWAIGHOFER, A., TRESP, V., AND YU, K. 2004. Learning Gaussian process kernels via hierarchical Bayes. Advances Neural Inform. Process. Syst. 17, 1209-1216.
37. UMYAROV, A. AND TUZHILIN, A. 2007. Leveraging aggregate ratings for better recommendations. In Proceedings of the ACM Conference on Recommender Systems, 161-164. (Pubitemid 351528429)
38. UMYAROV, A. AND TUZHILIN, A. 2008. Improving collaborative filtering recommendations using external data. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08). 618-627.
39. VERBEKE, G. AND MOLENBERGHS, G. 2000. Linear Mixed Models for Longitudinal Data. Springer Verlag.