NNIA-RS: A multi-objective optimization based recommender system

Physica A: Statistical Mechanics and its Applications

Volumn 424, Issue , 2015, Pages 383-397

  Geng, Bingrui ^a   Li, Lingling ^a   Jiao, Licheng ^a   Gong, Maoguo ^a   Cai, Qing ^a   Wu, Yue ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Accuracy; Diversity; Multi objective optimization; Novelty; Recommender systems

Indexed keywords

COLLABORATIVE FILTERING; EVOLUTIONARY ALGORITHMS; RECOMMENDER SYSTEMS;

ACCURACY; DIVERSITY; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; NONDOMINATED SOLUTIONS; NOVELTY; RECOMMENDATION ACCURACY; RECOMMENDATION DIVERSITIES;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84922756890     PISSN: 03784371     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physa.2015.01.007     Document Type: Article

References (48)

1. J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez Recommender systems survey Knowl.-Based Syst. 46 2013 109 132
2. M. Balabanović, and Y. Shoham Fab: content-based, collaborative recommendation Commun. ACM 40 3 1997 66 72
3. R. Van Meteren, M. Van Someren, Using content-based filtering for recommendation, in: Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, 2000.
4. J.B. Schafer, D. Frankowski, J. Herlocker, and S. Sen Collaborative filtering recommender systems The Adaptive Web 2007 Springer 291 324
5. A.B. Barragáns-Martínez, E. Costa-Montenegro, J.C. Burguillo, M. Rey-López, F.A. Mikic-Fonte, and A. Peleteiro A hybrid content-based and item-based collaborative filtering approach to recommend tv programs enhanced with singular value decomposition Inform. Sci. 180 22 2010 4290 4311
6. Y. Guan, D. Zhao, A. Zeng, and M.-S. Shang Preference of online users and personalized recommendations Physica A 392 16 2013 3417 3423
7. Z.-K. Zhang, L. Yu, K. Fang, Z.-Q. You, C. Liu, H. Liu, and X.-Y. Yan Website-oriented recommendation based on heat spreading and tag-aware collaborative filtering Physica A 399 0 2014 82 88
8. T. Zhou, J. Ren, M. Medo, and Y.-C. Zhang Bipartite network projection and personal recommendation Phys. Rev. E 76 4 2007 046115
9. J.-G. Liu, T. Zhou, H.-A. Che, B.-H. Wang, and Y.-C. Zhang Effects of high-order correlations on personalized recommendations for bipartite networks Physica A 389 4 2010 881 886
10. D. Wei, T. Zhou, G. Cimini, P. Wu, W. Liu, and Y.-C. Zhang Effective mechanism for social recommendation of news Physica A 390 11 2011 2117 2126
11. W. Zeng, Y.-X. Zhu, L. Lü, and T. Zhou Negative ratings play a positive role in information filtering Physica A 390 23 2011 4486 4493
12. L. Lü, M. Medo, C.H. Yeung, Y.-C. Zhang, Z.-K. Zhang, and T. Zhou Recommender systems Phys. Rep. 519 1 2012 1 49
13. S.-M. Choi, and Y.-S. Han Identifying representative ratings for a new item in recommendation system Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication 2013 ACM 64
14. A.M. Rashid, I. Albert, D. Cosley, S.K. Lam, S.M. McNee, J.A. Konstan, and J. Riedl Getting to know you: learning new user preferences in recommender systems Proceedings of the 7th international conference on Intelligent user interfaces 2002 ACM 127 134
15. M.-S. Shang, L. Lü, W. Zeng, Y.-C. Zhang, and T. Zhou Relevance is more significant than correlation: Information filtering on sparse data Europhys. Lett. 88 6 2009 68008
16. M. Ramezani, P. Moradi, and F. Akhlaghian A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains Physica A 408 2014 72 84
17. R.M. Bell, Y. Koren, Improved neighborhood-based collaborative filtering, in: KDD Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, sn, 2007.
18. J. Bennett, S. Lanning, The netflix prize, in: Proceedings of KDD Cup and Workshop, vol. 2007, 2007, p. 35.
19. S.M. McNee, J. Riedl, and J.A. Konstan Being accurate is not enough: how accuracy metrics have hurt recommender systems CHI'06 Extended Abstracts on Human Factors in Computing Systems 2006 ACM 1097 1101
20. T. Zhou, R.-Q. Su, R.-R. Liu, L.-L. Jiang, B.-H. Wang, and Y.-C. Zhang Accurate and diverse recommendations via eliminating redundant correlations New J. Phys. 11 12 2009 123008
21. P. Castells, S. Vargas, J. Wang, Novelty and diversity metrics for recommender systems: choice, discovery and relevance, in: International Workshop on Diversity in Document Retrieval (DDR 2011) at the 33rd European Conference on Information Retrieval (ECIR 2011), 2011.
22. Y. Cai, H.-f. Leung, Q. Li, H. Min, J. Tang, and J. Li Typicality-based collaborative filtering recommendation IEEE Trans. Knowl. Data Eng. 26 3 2014 766 779
23. T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, and Y.-C. Zhang Solving the apparent diversity-accuracy dilemma of recommender systems Proc. Natl. Acad. Sci. 107 10 2010 4511 4515
24. Z.-K. Zhang, T. Zhou, and Y.-C. Zhang Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs Physica A 389 1 2010 179 186
25. T. Jambor, and J. Wang Optimizing multiple objectives in collaborative filtering Proceedings of the Fourth ACM Conference on Recommender Systems 2010 ACM 55 62
26. L. Lü, and W. Liu Information filtering via preferential diffusion Phys. Rev. E 83 2011 066119
27. N. Hurley, and M. Zhang Novelty and diversity in top-n recommendation-analysis and evaluation ACM Trans. Internet Technol. 10 4 2011 14
28. M.T. Ribeiro, A. Lacerda, A. Veloso, and N. Ziviani Pareto-efficient hybridization for multi-objective recommender systems Proceedings of the Sixth ACM Conference on Recommender Systems 2012 ACM 19 26
29. Y. Zhou, L. Lü, W. Liu, and J. Zhang The power of ground user in recommender systems PLoS One 8 8 2013 e70094
30. A. Mikeli, D. Apostolou, D. Despotis, A multi-criteria recommendation method for interval scaled ratings, in: Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on, vol. 3, IEEE, 2013, pp. 9-12.
31. X. Su, and T.M. Khoshgoftaar A survey of collaborative filtering techniques Adv. Artif. Intell. 2009 2009 4
32. Y. Koren, The bellkor solution to the netflix grand prize, Netflix prize documentation.
33. Y. Koren Factor in the neighbors: Scalable and accurate collaborative filtering ACM Trans. Knowl. Discov. Data 4 1 2010 1
34. M. Deshpande, and G. Karypis Item-based top-n recommendation algorithms ACM Trans. Inf. Syst. 22 1 2004 143 177
35. P. Cremonesi, Y. Koren, and R. Turrin Performance of recommender algorithms on top-n recommendation tasks Proceedings of the Fourth ACM Conference on Recommender Systems 2010 ACM 39 46
36. M. Zhang Enhancing diversity in top-n recommendation Proceedings of the Third ACM Conference on Recommender Systems 2009 ACM 397 400
37. S. Vargas, and P. Castells Rank and relevance in novelty and diversity metrics for recommender systems Proceedings of the Fifth ACM Conference on Recommender Systems 2011 ACM 109 116
38. M. Gong, L. Jiao, H. Du, and L. Bo Multiobjective immune algorithm with nondominated neighbor-based selection Evol. Comput. 16 2 2008 225 255
39. E. Zitzler, M. Laumanns, L. Thiele, E. Zitzler, E. Zitzler, L. Thiele, L. Thiele, Spea2: Improving the strength pareto evolutionary algorithm, 2001.
40. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan A fast and elitist multiobjective genetic algorithm: Nsga-ii IEEE Trans. Evol. Comput. 6 2 2002 182 197
41. Q. Zhang, and H. Li Moea/d: A multiobjective evolutionary algorithm based on decomposition IEEE Trans. Evol. Comput. 11 6 2007 712 731
42. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl Item-based collaborative filtering recommendation algorithms Proceedings of the 10th International Conference on World Wide Web 2001 ACM 285 295
43. J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl Evaluating collaborative filtering recommender systems ACM Trans. Inf. Syst. 22 1 2004 5 53
44. Z.-K. Zhang, C. Liu, Y.-C. Zhang, and T. Zhou Solving the cold-start problem in recommender systems with social tags Europhys. Lett. 92 2 2010 28002
45. T. Zhou, L.-L. Jiang, R.-Q. Su, and Y.-C. Zhang Effect of initial configuration on network-based recommendation Europhys. Lett. 81 5 2008 58004
46. X. Ren, L. Lü, R. Liu, and J. Zhang Avoiding congestion in recommender systems New J. Phys. 16 6 2014 063057
47. D. Brockhoff, T. Friedrich, and F. Neumann Analyzing hypervolume indicator based algorithms Parallel Problem Solving from Nature-PPSN X 2008 Springer 651 660
48. Y. Koren, R. Bell, and C. Volinsky Matrix factorization techniques for recommender systems Computer 42 8 2009 30 37