A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

IEEE Transactions on Neural Networks and Learning Systems

Volumn 27, Issue 3, 2016, Pages 579-592

  Luo, Xin ^a   Zhou, Meng Chu ^b,c   Li, Shuai ^d   You, Zhuhong ^d   Xia, Yunni ^a   Zhu, Qingsheng ^a  

^a CHONGQING UNIVERSITY   (China)

^b NEW JERSEY INSTITUTE OF TECHNOLOGY   (United States)

^c Department of Computer Science and Technology   (China)

^d HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Alternating direction method; big data; collaborative filtering; recommender system; sparse matrices.

Indexed keywords

BIG DATA; COLLABORATIVE FILTERING; DIGITAL STORAGE; FACTORIZATION; RECOMMENDER SYSTEMS;

ALTERNATING DIRECTION METHODS; CONVERGENCE RATES; LATENT FACTOR MODELS; NON-NEGATIVITY CONSTRAINTS; NONNEGATIVE MATRIX FACTORIZATION; PREDICTION ACCURACY; REAL APPLICATIONS; STORAGE COMPLEXITY;

MATRIX ALGEBRA;

EID: 84929832189     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2015.2415257     Document Type: Article

References (59)

1. P. Resnick and H. R. Varian, "Recommender systems, " Commun. ACM, vol. 40, no. 3, pp. 56-58, 1997.
2. 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. Knowl. Data Eng., vol. 17, no. 6, pp. 734-749, Jun. 2005.
3. F. Dinuzzo, G. Pillonetto, and G. De Nicolao, "Client-server multitask learning from distributed datasets, " IEEE Trans. Neural Netw., vol. 22, no. 2, pp. 290-303, Feb. 2011.
4. J. L. De La Rosa, N. Hormazabal, S. Aciar, G. Lopardo, A. Trias, and M. Montaner, "A negotiation-style recommender based on computational ecology in open negotiation environments, " IEEE Trans. Ind. Electron., vol. 58, no. 6, pp. 2073-2085, Jun. 2011.
5. Y. Li et al., "An efficient recommendation method for improving business process modeling, " IEEE Trans. Ind. Informat., vol. 10, no. 1, pp. 502-513, Feb. 2014.
6. Y. Cai, H.-F. Leung, Q. Li, H. Min, J. Tang, and J. Li, "Typicality-based collaborative filtering recommendation, " IEEE Trans. Knowl. Data Eng., vol. 26, no. 3, pp. 766-779, Mar. 2014.
7. J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering, " in Proc. 14th Conf. Uncertainty Artif. Intell., Madison, WI, USA, Jul. 1998, pp. 43-52.
8. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, "Item-based collaborative filtering recommendation algorithms, " in Proc. 10th Int. World Wide Web Conf., Hong Kong, May 2001, pp. 285-295.
9. X. Su and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques, " Adv. Artif. Intell., vol. 2009, pp. 1-19, Jan. 2009.
10. Y. Koren, "Collaborative filtering with temporal dynamics, " Commun. ACM, vol. 53, no. 4, pp. 89-97, 2010.
11. Y. Koren and R. Bell, "Advances in collaborative filtering, " in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. New York, NY, USA: Springer-Verlag, 2011.
12. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, "Application of dimensionality reduction in recommender systems-A case study, " in Proc. ACM WebKDD Web Mining E-Commerce Workshop, Boston, MA, USA, Aug. 2000, pp. 285-295.
13. G. Takács, I. Pilászy, B. Németh, and D. Tikk, "Scalable collaborative filtering approaches for large recommender systems, " J. Mach. Learn. Res., vol. 10, pp. 623-656, Mar. 2009.
14. Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems, " Computer, vol. 42, no. 8, pp. 30-37, Aug. 2009.
15. R. R. Salakhutdinov and A. Mnih, "Probabilistic matrix factorization, " in Advances in Neural Information Processing Systems, vol. 20. Red Hook, NY, USA: Curran Associates, 2008, pp. 1257-1264.
16. K. Yu, S. Zhu, J. Lafferty, and Y. Gong, "Fast nonparametric matrix factorization for large-scale collaborative filtering, " in Proc. 32nd Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., Boston, MA, USA, Jul. 2009, pp. 211-218.
17. W. Chu and Z. Ghahramani, "Probabilistic models for incomplete multi-dimensional arrays, " in Proc. 12th Int. Conf. Artif. Intell. Statist., Clearwater Beach, FL, USA, Apr. 2009, pp. 89-96.
18. N. Srebro and R. R. Salakhutdinov, "Collaborative filtering in a non-uniform world: Learning with the weighted trace norm, " in Advances in Neural Information Processing Systems, vol. 23. Vancouver, BC, Canada, Dec. 2010, pp. 2056-2064.
19. N. Houlsby, F. Huszar, Z. Ghahramani, and J. M. Hernández-Lobato, "Collaborative Gaussian processes for preference learning, " in Advances in Neural Information Processing Systems, vol. 25. Lake Tahoe, NV, USA, Dec. 2012, pp. 2105-2113.
20. S. Chatzis, "Nonparametric Bayesian multitask collaborative filtering, " in Proc. 22nd ACM Int. Conf. Inf. Knowl. Manage., San Francisco, CA, USA, Oct. 2013, pp. 2149-2158.
21. J. M. Hernández-Lobato, N. Houlsby, and Z. Ghahramani, "Probabilistic matrix factorization with non-random missing data, " in Proc. 31st Int. Conf. Mach. Learn., Beijing, China, Jun. 2014, pp. 1512-1520.
22. N. Zhou, W. K. Cheung, G. Qiu, and X. Xue, "A hybrid probabilistic model for unified collaborative and content-based image tagging, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 7, pp. 1281-1294, Jul. 2011.
23. S. Boutemedjet and D. Ziou, "Predictive approach for user long-term needs in content-based image suggestion, " IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 8, pp. 1242-1253, Aug. 2012.
24. J. Wu, L. Chen, Y. Feng, Z. Zheng, M. C. Zhou, and Z. Wu, "Predicting quality of service for selection by neighborhood-based collaborative filtering, " IEEE Trans. Syst., Man, Cybern., Syst., vol. 43, no. 2, pp. 428-439, Mar. 2013.
25. Z. Zheng, H. Ma, M. R. Lyu, and I. King, "Collaborative web service QoS prediction via neighborhood integrated matrix factorization, " IEEE Trans. Services Comput., vol. 6, no. 3, pp. 289-299, Jul./Sep. 2012.
26. M.-F. Weng and Y.-Y. Chuang, "Collaborative video reindexing via matrix factorization, " ACM Trans. Multimedia Comput., Commun., Appl., vol. 8, no. 2, 2012, Art. ID 23.
27. J. J. Pan, S. J. Pan, J. Yin, L. M. Ni, and Q. Yang, "Tracking mobile users in wireless networks via semi-supervised colocalization, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 3, pp. 587-600, Mar. 2012.
28. S. Zhang, W. Wang, J. Ford, and F. Makedon, "Learning from incomplete ratings using non-negative matrix factorization, " in Proc. 6th SIAM Int. Conf. Data Mining, Bethesda, MD, USA, Apr. 2006, pp. 549-553.
29. M. Wu, "Collaborative filtering via ensembles of matrix factorizations, " in Proc. KDD Cup Workshop, San Jose, CA, USA, Aug. 2007, pp. 43-47.
30. G. Chen, F. Wang, and C. Zhang, "Collaborative filtering using orthogonal nonnegative matrix tri-factorization, " Inf. Process. Manage., vol. 45, no. 3, pp. 368-379, 2009.
31. Q. Gu, J. Zhou, and C. Ding, "Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs, " in Proc. SIAM Int. Conf. Data Mining, Columbus, OH, USA, Apr. 2010, pp. 199-210.
32. X. Luo, M. Zhou, Y. Xia, and Q. Zhu, "An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems, " IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1273-1284, May 2014.
33. P. Paatero and U. Tapper, "Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, " Environmetrics, vol. 5, no. 2, pp. 111-126, 1994.
34. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization, " Nature, vol. 401, pp. 788-791, Oct. 1999.
35. C.-J. Lin, "Projected gradient methods for nonnegative matrix factorization, " Neural Comput., vol. 19, no. 10, pp. 2756-2779, 2007.
36. C. Ding, T. Li, and M. I. Jordan, "Convex and semi-nonnegative matrix factorizations, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 45-55, Jan. 2010.
37. N. Guan, D. Tao, Z. Luo, and B. Yuan, "Online nonnegative matrix factorization with robust stochastic approximation, " IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 7, pp. 1087-1099, Jul. 2012.
38. A. Cichocki, R. Zdunek, A. H. Phan, and S.-I. Amari, Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation. Natick, MA, USA: Wiley, 2009.
39. Y. Xu, W. Yin, Z. Wen, and Y. Zhang, "An alternating direction algorithm for matrix completion with nonnegative factors, " Frontiers Math. China, vol. 7, no. 2, pp. 365-384, 2012.
40. J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, "GroupLens: Applying collaborative filtering to Usenet news, " Commun. ACM, vol. 40, no. 3, pp. 77-87, 1997.
41. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, "Distributed optimization and statistical learning via the alternating direction method of multipliers, " Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1-122, 2011.
42. N. Zheng, Q. Li, S. Liao, and L. Zhang, "Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr, " J. Inf. Sci., vol. 36, no. 6, pp. 733-750, 2010.
43. H. Ma, I. King, and M. R. Lyu, "Learning to recommend with explicit and implicit social relations, " ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1-19, 2011.
44. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, "Evaluating collaborative filtering recommender systems, " ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 5-53, 2004.
45. G. Shani and A. Gunawardana, "Evaluating recommendation systems, " in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. New York, NY, USA: Springer-Verlag, 2011.
46. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, "Eigentaste: A constant time collaborative filtering algorithm, " Inf. Retr., vol. 4, no. 2, pp. 133-151, 2001.
47. T. G. Dietterich, "Ensemble methods in machine learning, " in Proc. 1st Int. Workshop Multiple Classifier Syst., Cagliari, Italy, Jun. 2000, pp. 1-15.
48. L. Xin, Y. Ouyang, and X. Zhang, "Improving latent factor model based collaborative filtering via integrated folksonomy factors, " Int. J. Uncertainty, Fuzziness, Knowl.-Based Syst., vol. 19, no. 2, pp. 307-327, 2011.
49. X. Cao, X. Wang, D. Jin, Y. Cao, and D. He, "Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization, " Sci. Rep., vol. 3, pp. 1-8, Oct. 2013.
50. Y. Hu, Y. Koren, and C. Volinsky, "Collaborative filtering for implicit feedback datasets, " in Proc. 8th IEEE Int. Conf. Data Mining, Pisa, Italy, Dec. 2008, pp. 263-272.
51. C. Chen, D. Neal, and M. Zhou, "Understanding the evolution of a disaster-A framework for assessing crisis in a system environment (FACSE), " Natural Hazards, vol. 65, no. 1, pp. 407-422, Jan. 2013.
52. W. Guo, W. M. Healy, and M. C. Zhou, "Impacts of 2.4 GHz ISM band interference on IEEE 802.15.4 wireless sensor network reliability in buildings, " IEEE Trans. Instrum. Meas., vol. 61, no. 9, pp. 2533-2544, Sep. 2012.
53. C. Jiang, H. Sun, Z. Ding, P. Wang, and M. C. Zhou, "An indexing network: Model and applications, " IEEE Trans. Syst., Man, Cybern., Syst., vol. 44, no. 12, pp. 1584-1597, Dec. 2014.
54. S. Liu, H. Cao, L. Li, and M. C. Zhou, "Predicting stay time of mobile users with contextual information, " IEEE Trans. Autom. Sci. Eng., vol. 10, no. 4, pp. 1026-1036, Oct. 2013.
55. C. Shang, M. C. Zhou, and C. Chen, "Cellphone Data and Applications, " Int. J. Intell. Control Syst., vol. 19, no. 1, pp. 35-45, Mar. 2014.
56. W. Tan and M. Zhou, "Service Oriented Workflow Systems, " in Contemporary Issues in Systems Science and Engineering, M. C. Zhou, H.-X. Li, and M. Weijnen, Eds. Hoboken, NJ, USA: Wiley, 2015, pp. 645-660.
57. Y. Wu et al., "A novel method for calculating service reputation, " IEEE Trans. Autom. Sci. Eng., vol. 10, no. 3, pp. 634-642, Jul. 2013.
58. M. Yu and M. C. Zhou, "A performance modeling scheme for multistage switch networks with phase-type and bursty traffic, " IEEE/ACM Trans. Netw., vol. 18, no. 4, pp. 1091-1104, Aug. 2010.
59. M. Yu, X. Ma, and M. C Zhou, "Radio channel allocations with global optimality and bounded computational scale, " IEEE Trans. Veh. Technol., vol. 63, no. 9, pp. 4670-4680, Nov. 2014.