Recommendation using neighborhood methods with preference-relation-based similarity

Information Sciences

Volumn 284, Issue , 2014, Pages 18-30

  Hu, Yi Chung ^a  

^a CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

Author keywords

Collaborative filtering; MCDM; Neighborhood method; Preference relation; Recommender system

Indexed keywords

COLLABORATIVE FILTERING; RECOMMENDER SYSTEMS;

GENERALIZATION ABILITY; MCDM; MULTI-CRITERIA; NEIGHBORHOOD METHODS; POTENTIAL CUSTOMERS; PREFERENCE RELATION; SIMILARITY MEASURE; USER'S PREFERENCES;

CATCHMENTS;

EID: 84906685731     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2014.06.043     Document Type: Article

References (53)

1. G. Adomavicius, and Y. Kwon New recommendation techniques for multicriteria rating systems IEEE Intell. Syst. 2007 48 55
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. 17 6 2005 734 749
3. A. Albadvi, S.K. Chaharsooghi, and A. Esfahanipour Decision making in stock trading: an application of PROMETHEE Eur. J. Oper. Res. 177 2 2007 673 683
4. BIA Kelsey, 2011. < http://www.biakelsey.com >.
5. J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal A collaborative filtering approach to mitigate the new user cold start problem Knowl.-Based Syst. 26 2012 225 238
6. J.P. Brans, B. Marechal, and Ph. Vincke PROMETHEE: a new family of outranking methods in multicriteria analysis Oper. Res. Int. J. 84 1984 477 490
7. J.P. Brans, and Ph. Vincke A preference ranking organization method Manage. Sci. 31 6 1985 647 656
8. J.P. Brans, Ph. Vincke, and B. Marechal How to select and how to rank projects: the PROMETHEE method Eur. J. Oper. Res. 24 2 1986 228 238
9. K. Choi, and Y. Suh A new similarity function for selecting neighbors for each target item in collaborative filtering Knowl.-Based Syst. 37 2013 146 153
10. J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
11. J. Figueira, V. Mousseau, and B. Roy ELECTRE methods J. Figueira, S. Greco, M. Ehrgott, Multiple Criteria Decision Analysis: State of The Art Surveys 2004 Springer 163 198
12. M. Friedman A comparison of alternative tests of significance for the problem of m rankings Ann. Math. Stat. 11 1940 86 92
13. D.E. Goldberg Genetic Algorithms in Search, Optimization, and Machine Learning 1989 Addison-Wesley MA
14. J.B. Grau, J.M. Anton, A.M. Tarquis, and D. Andina Election of water resources management entity using a multi-criteria decision (MCD) method in Salta province (Argentine) J. Syst. Cybern. Inform. 7 4 2009 1 7
15. J. Herlocker, J. Konstan, A. Borchers, J. Riedl, An algorithmic framework for performing collaborative filtering, in: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, pp. 230-237.
16. J. Herlocker, J. Konstan, and J. Riedl An empirical analysis of design choices in neighborhood-based collaborative filtering Inf. Retrieval 5 4 2002 287 310
17. C. Hermans, J. Erickson, T. Noordewier, A. Sheldon, and M. Kline Collaborative environmental planning in river management: an application of multicriteria decision analysis in the White River Watershed in Vermont J. Environ. Manage. 84 4 2007 534 546
18. Y.C. Hu, S.T. Sun, Applying multi-criteria rating systems to initiator recommendation on a group-buying website, in: Proceedings of the International Conference on Information Management, Kaohsiung, Taiwan, 2012.
19. R.L. Iman, and J.M. Davenport Approximations of the critical region of the Friedman statistic Commun. Stat. 9 6 1980 571 595
20. H. Ishibuchi, T. Nakashima, and M. Nii Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining 2004 Springer
21. D. Jannach, Z. Karakaya, F. Gedikli, Accuracy improvements for multi-criteria recommender systems, in: Proceedings of the 13th ACM Conference on Electronic Commerce New York, USA, 2012, pp. 674-689.
22. D. Jannach, M. Zanker, A. Felfernig, G. Friedrich, Recommender Systems: An Introduction, Cambridge, NY, 2010.
23. J. Konstan, B. Miller, D. Maltz, J. Herlocker, G. Lee, and J. Riedl GroupLens: applying collaborative filtering to Usenet news Commun. ACM 40 3 1997 77 87
24. Y. Koren, R. Bell, and C. Volinsky Matrix factorization techniques for recommender systems Computer 42 8 2009 30 37
25. C.H. Lai, D.R. Liu, and C.S. Lin Novel personal and group-based trust models in collaborative filtering for document recommendation Inf. Sci. 239 2013 31 49
26. W. Lee, C. Liu, and C. Lu Intelligent agent-based systems for personalized recommendations in Internet commerce Expert Syst. Appl. 22 4 2002 275 284
27. N. Manouselis, and C. Costopoulou Experimental analysis of design choices in multi-attribute utility collaborative filtering Int. J. Pattern Recognit. Artif. Intell. 21 2 2007 311 331
28. S. Opricovic, and G.H. Tzeng Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS Eur. J. Oper. Res. 156 2004 445 455
29. S. Opricovic, and G.H. Tzeng Extended VIKOR method in comparison with outranking methods Eur. J. Oper. Res. 178 2007 514 529
30. F. Ortega, J. Sánchez, J. Bobadilla, and A. Gutiérrez Improving collaborative filtering-based recommender systems results using Pareto dominance Inf. Sci. 239 2013 50 61
31. A. Osyczka Evolutionary Algorithms for Single and Multicriteria Design Optimization 2002 Physica-Verlag NY
32. P. Perny Multicriteria filtering methods based on concordance and non-discordance principles Ann. Oper. Res. 80 1998 137 165
33. P. Perny, and B. Roy The use of fuzzy outranking relations in preference modeling Fuzzy Sets Syst. 49 1992 33 53
34. C. Porcel, and E. Herrera-Viedma Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries Knowl.-Based Syst. 23 1 2010 32 39
35. F. Ricci, L. Rokach, B. Shapira, and P.B. Kantor Recommender Systems Handbook 2011 Springer NY
36. F. Ricci, and H. Werthner Case-based querying for travel planning recommendation Inf. Technol. Tourism 4 3-4 2002 215 226
37. B. Roy Classement et choix en présence de points de vue multiple (La méthode Electre) Rev. Inform. Rech. Opér. 8 1968 57 75
38. B. Roy Multicriteria Methodology for Decision Aiding 1996 Kluwer
39. B. Roy ELECTRE III: un algorithme de classement fondé sur une représentation floue des préférences en présence de critères multiples Cah. CERO 20 1978 3 24
40. J.B. Schafer, DynamicLens: a dynamic user-interface for a meta-recommendation system, in: Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research, 2005, pp. 72-76.
41. J.B. Schafer, D. Frankowski, J. Herlocker, S. Sen, Collaborative filtering recommender systems, in: The Adaptive Web: Methods and Strategies of Web Personalization, LNCS, vol. 4321, 2007, pp. 291-324.
42. D. Simon Evolutionary Optimization Algorithms 2013 John Wiley & Sons New York
43. P. Vincke Multicriteria Decision Aid 1992 John Wiley New York
44. J. Yan, T. Dagang, and P. Yue Ranking environmental projects model based on multicriteria decision-making and the weight sensitivity analysis J. Syst. Eng. Electron. 18 3 2007 534 539
45. J. Zhang, Q. Peng, S. Sun, and C. Liu Collaborative filtering recommendation algorithm based on user preference derived from item domain Physica A 396 2014 66 76
46. Y. Zhang, Y. Zhuang, J. Wu, and L. Zhang Applying probabilistic latent semantic analysis to multi-criteria recommender system AI Commun. 22 2 2009 97 107
47. K.F. Man, K.S. Tang, and S. Kwong Genetic Algorithms: Concepts and Designs 1999 Springer London
48. J. Bobadilla, F. Ortega, A. Hernando, and J. Alcalá Improving collaborative filtering recommender system results and performance using genetic algorithms Knowl.-Based Syst. 24 8 2011 1310 1316
49. J. Bobadilla, F. Ortega, and A. Hernando A collaborative filtering similarity measure based on singularities Inf. Process. Manage. 48 2012 204 217
50. F. Ortega, J.L. Sánchez, J. Bobadilla, and A. Gutiérrez Improving collaborative filtering-based recommender systems results using Pareto dominance Inf. Sci. 239 2013 50 61
51. A. Tejeda-Lorente, C. Porcel, E. Peis, R. Sanz, and E. Herrera-Viedma A quality based recommender system to disseminate information in a University Digital Library Inf. Sci. 261 2014 52 69
52. C. Porcel, A. Tejeda-Lorente, M.A. Martínez, and E. Herrera-Viedma A hybrid recommender system for the selective dissemination of research resources in a technology transfer office Inf. Sci. 184 1 2012 1 19
53. J. Serrano-Guerrero, E. Herrera-Viedma, J.A. Olivas, A. Cerezo, and F.P. Romero A Google wave-based fuzzy recommender system to disseminate information in university digital libraries 2.0 Inf. Sci. 181 2011 1503 1516