A learning method for Top-N recommendations with incomplete data

Social Network Analysis and Mining

Volumn 3, Issue 4, 2013, Pages 1135-1148

  Ren, Yongli ^a   Li, Gang ^a   Zhou, Wanlei ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

Rating patterns; Top N recommendations

Indexed keywords

COLLABORATIVE FILTERING; MATRIX ALGEBRA;

INCOMPLETE DATA; LEARNING METHODS; MISSING VALUES; RATING PATTERN; REAL DATA SETS; RECOMMENDATION METHODS; STATE OF THE ART; TOP-N RECOMMENDATIONS;

ITERATIVE METHODS;

EID: 84947246649     PISSN: 18695450     EISSN: 18695469     Source Type: Journal    
DOI: 10.1007/s13278-013-0103-2     Document Type: Article

References (32)

1. Agarwal V, Bharadwaj KK (2012) A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. Soc Netw Anal Min. doi:10.1007/s13278-012-0083-7
2. Akcora CG, Carminati B, Ferrari E (2013) User similarities on social networks. Soc Netw Anal Min. doi:10.1007/s13278-012-0090-8
3. Anderson C (2006) The Long Tail: Why the Future of Business Is Selling Less of More, vol 33. Hyperion, New York
4. Azar Y, Fiat A, Karlin AR, McSherry F, Saia J (2001) Spectral Analysis of Data. In: Proceedings of the thirty-third annual ACM symposium on Theory of computing, pp 619–626
5. Bento J, Fawaz N, Montanari A, Ioannidis S (2011) Identifying Users From Their Rating Patterns. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, pp 39–46
6. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022
7. Cremonesi P, Koren Y, Turrin R (2010) Performance of Recommender Algorithms on Top-N Recommendation Tasks. In: Recsys, ACM, New York, pp 39–46
8. Cremonesi P, Garzotto F, Negro S, Papadopoulos A (2011) Comparative evaluation of recommender system quality. In: Proceedings of CHI Extended Abstracts, pp 1927–1932
9. Deshpande M, Karypis G (2004) Item-based Top-N recommendation algorithms. ACM TOIS 22(1):143–177
10. Esslimani I, Brun A, Boyer A (2010) Densifying a behavioral recommender system by social networks link prediction methods. Soc Netw Anal Min 1(3):159–172
11. Funk S (2006) Netflix update: try this at home. http://sifter.org/~simon/journal/20061211.html
12. Geng X, Zhou ZH, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE TPAMI 29(12):2234–2240
13. Golub GH, Loan CFV (1996) Introduction to Matrix, vol 1, 3rd edn. The Johns hopkins University Press, Baltimore
14. Granka LA, Joachims T, Gay G (2004) Eye-Tracking Analysis of User Behavior in WWW Search. In: SIGIR 2004, pp 478–479
15. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inform Syst 22(1):5–53
16. Hofmann T (2004) Latent Semantic Models for Collaborative Filtering. ACM Trans Inform Syst 22(1):89–115
17. Karypis G (2001) Evaluation of Item-Based Top-N Recommendation Algorithms. In: CIKM 2001, ACM, New York, pp 247–254
18. Kim D, Yum Bj (2005) Collaborative Filtering based on Iterative Principal Component Analysis. Expert Syst Appl 28:823–830
19. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD 2008. ACM, New York, pp 426–434
20. Koren Y (2009) Collaborative Filtering with Temporal Dynamics. In: SIGKDD 2009. ACM, New York, pp 447–456
21. Leonardis A (2000) Robust recognition using eigenimages. Comput Vis Image Underst 78(1):99–118
22. Li C, Datta A, Sun A (2011) Mining latent relations in peer-production environments: a case study with Wikipedia article similarity and controversy. Soc Netw Anal Min 2(3):265–278
23. Ma H, King I, Lyu MR (2007) Effective missing data prediction for collaborative filtering. In: SIGIR 2007, ACM Press, New York, pp 39–46
24. Ning X, Karypis G (2011) SLIM: Sparse linear methods for Top-N recommender systems. In: ICDM 2011, pp 497–506
25. Niu W, Lei J, Tong E, Li G, Chang L, Shi Z, Ci S (2013) Context-aware service ranking in wireless sensor networks. J Netw Syst Manag. doi:10.1007/s10922-012-9259-8
26. Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop 2007, pp 5–8
27. Radlinski F, Joachims T (2005) Query chains: learning to rank from implicit feedback. In: SIGKDD 2005, pp 239–248
28. Ren Y, Li G, Zhang J, Zhou W (2012) Lazy Collaborative filtering for data sets with missing values. IEEE Trans Syst Man Cybern Part B: Cybern
29. Rettinger A, Wermser H, Huang Y, Tresp V (2012) Context-aware tensor decomposition for relation prediction in social networks. Soc Netw Anal Min 2(4):373–385
30. Sarwar B, Karypis G, Konstan J, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. ACM, New York, pp 285–295
31. Sarwar BM, Karypis G, Konstan JA, Riedl JT (2000) Application of dimensionality reduction in recommender system—a case study. In: ACM WebKDD Workshop
32. Vu H, Li G, Sukhorukova N, Beliakov G (2012) K-complex detection using a hybrid-synergic machine learning method. IEEE Trans Syst Man Cybern PartC: Appl Rev 42(6):1478–1490