Two algorithms under stochastic gradient descent framework for recommender systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8643, Issue , 2014, Pages 219-230

  Huang, Tian Hsiang ^a   Nikulin, Vladimir ^b  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

^b VYATKA STATE UNIVERSITY   (Russian Federation)

Author keywords

Chess ratings; Collaborative filtering; Matrix factorization; Online education; Pairwise coupling; Recommender system; Unsupervised learning

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BEHAVIORAL RESEARCH; BIG DATA; BIOINFORMATICS; COLLABORATIVE FILTERING; DISTANCE EDUCATION; DISTRIBUTED COMPUTER SYSTEMS; FACTORIZATION; FORECASTING; GRADIENT METHODS; INFORMATION MANAGEMENT; LEARNING SYSTEMS; MATRIX ALGEBRA; MOBILE COMMERCE; PUBLIC HEALTH; QUALITY OF SERVICE; RECOMMENDER SYSTEMS; SOCIAL NETWORKING (ONLINE); SOCIAL SCIENCES COMPUTING; STOCHASTIC SYSTEMS; STUDENTS; UNSUPERVISED LEARNING;

MATRIX FACTORIZATIONS; ON-LINE EDUCATION; PAIRWISE COUPLINGS; STANDARD FACTORIZATION; STATE OF THE ART; STOCHASTIC GRADIENT DESCENT; STOCHASTIC GRADIENT METHODS; STUDENT EVALUATION;

DATA MINING;

EID: 84915753623     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-13186-3_21     Document Type: Conference Paper

References (15)

1. Takacs, G., Pilaszy, I., Nemeth, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)
2. Ricci, F., Rokach, L., Shapira, B., Kantor, P.: Recommender Systems Handbook. Springer, Heidelberg (2011)
3. Zhuang, Y., Chin, W.S., Juan, Y.C., Lin, C.J.: A fast parallel SGD for matrix factorization in shared memory systems. In: RecSys 2013, October 12–16, 2013, Hong Kong, China (2013)
4. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems an Introduction. Cambridge University Press, New York (2010)
5. Oja, E., Ilin, A., Luttinen, J., Yang, Z.: Linear expansions with nonlinear cost functions: modelling, representation, and partitioning. In: Aranda, J., Xambo, S. (eds.) Plenary and Invited Lectures, WCCI 2010, Barcelona, Spain, pp. 105–123 (2010)
6. Lee, D., Seung, H.: Learning the parts of objects by nonnegative matrix factorization. Nat. 401, 788–791 (1999)
7. Gemulla, R., Haas, P., Nijkamp, E., Sismanis, Y.: Large-scale matrix factorization with distributed stochastic gradient descent. In: KDD 2011 Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 69–77 (2011)
8. Nikulin, V., McLachlan, G.: Classification of imbalanced marketing data with balanced random sets. JMLR Workshop Conf. Proc. 7, 89–100 (2009)
9. Nikulin, V., Huang, T.H.: Unsupervised dimensionality reduction via gradientbased matrix factorization with two learning rates and their automatic updates. J. Mach. Learn. Res. Workshop Conf. Proc. 27, 181–195 (2012)
10. Sismanis, Y.: How i won the chess ratings - Elo vs the rest of the world competition. In: arXiv:1012.4571v1 (2010)
11. Takacs, G., Pilaszy, I., Nemeth, B., Tikk, D.: On the gravity recommendation system. In: KDD Cup Workshop at SIGKDD 2007 San Jose, California USA, pp. 22–30 (2007)
12. Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3, 1–22 (2012)
13. Glickman, M.E.: Parameter estimation in large dynamic paired comparison experiments. Appl. Statist. 48(3), 377–394 (1999)
14. Huang, T.K., Weng, R., Lin, C.J.: Generalized Bradley-Terry models and multiclass probability estimates. J. Mach. Learn. Res. 7, 85–115 (2006)
15. Nikulin, V., Bakharia, A., Huang, T.-H.: On the evaluation of the homogeneous ensembles with CV-passports. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013 Workshops. LNCS, vol. 7867, pp. 109–120. Springer, Heidelberg (2013)