A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques

Soft Computing

Volumn 19, Issue 11, 2015, Pages 3173-3207

  Nilashi, Mehrbakhsh ^a   Ibrahim, Othman Bin ^a   Ithnin, Norafida ^a   Zakaria, Rozana ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Adaptive Neuro Fuzzy inference systems; Classification; Higher order singular value decomposition; Multi criteria collaborative filtering; Predictive accuracy

Indexed keywords

BEHAVIORAL RESEARCH; CLASSIFICATION (OF INFORMATION); FUZZY INFERENCE; FUZZY SYSTEMS; KNOWLEDGE BASED SYSTEMS; PROBLEM SOLVING; SCALABILITY; SINGULAR VALUE DECOMPOSITION;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION TECHNIQUES; HIGHER ORDER SINGULAR VALUE DECOMPOSITION; MULTI-CRITERIA; NEURO-FUZZY TECHNIQUES; PREDICTIVE ACCURACY; RECOMMENDATION TECHNIQUES;

COLLABORATIVE FILTERING;

EID: 84942193459     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-014-1475-6     Document Type: Article

References (112)

1. Adomavicius G, Kwon Y (2007) New recommendation techniques for multicriteria rating systems. Intell Syst IEEE 22(3):48–55
2. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowl Data Eng IEEE Trans 17(3):734–749
3. Ahmad W, Khokhar A (2007) An architecture for privacy preserving collaborative filtering on web portals. In: information assurance and security. IAS 2007. Third international symposium on, 2007. IEEE, pp 273–278
4. Amatriain X, Jaimes A, Oliver N, Pujol JM (2011) Data mining methods for recommender systems. In: recommender systems handbook. Springer, pp 39–71
5. Avci E (2008) Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Appl Soft Comput 8(1):225–231
6. Bagherifard K, Nilashi M, Ibrahim O, Ithnin N, Nojeem LA (2013) Measuring semantic similarity in grids using ontology. Int J Innov Appl Stud 2(3):230–237
7. Bellocchio F, Ferrari S, Piuri V, Borghese NA (2012) Hierarchical approach for multiscale support vector regression. Neural Netw Learn Syst IEEE Trans 23(5):1448–1460
8. Bilge A, Polat H (2013) A scalable privacy-preserving recommendation scheme via bisecting k-means clustering. Inf Process Manag 49(4):912–927
9. Billsus D, Pazzani MJ (2000) Learning collaborative information filters. In: Proceedings of the fifteenth international conference on machine learning, p 48
10. Bobadilla J, Ortega F, Hernando A (2012) A collaborative filtering similarity measure based on singularities. Inf Process Manag 48(2):204–217
11. Bobadilla J, Ortega F, Hernando A, Alcalá J (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl Based Syst 24(8):1310–1316
12. Bouchachia A, Pedrycz W (2006) Enhancement of fuzzy clustering by mechanisms of partial supervision. Fuzzy Sets Syst 157(9):1733–1759
13. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., pp 43–52
14. Buragohain M, Mahanta C (2008) A novel approach for ANFIS modelling based on full factorial design. App Soft Comput 8(1):609–625
15. Cao Y, Li Y (2007) An intelligent fuzzy-based recommendation system for consumer electronic products. Expert syst Appl 33(1):230–240
16. Carbo J, Molina JM (2004) Agent-based collaborative filtering based on fuzzy recommendations. Int J Web Eng Technol 1(4):414–426
17. Castellano G, Fanelli A, Torsello M (2007) A neuro-fuzzy collaborative filtering approach for web recommendation. Int J Comput Sci 1(1):27–29
18. Cechinel C, Sicilia M-Á, Sánchez-Alonso S, García-Barriocanal E (2013) Evaluating collaborative filtering recommendations inside large learning object repositories. Inf Process Manag 49(1):34–50
19. Cetişli B, Barkana A (2010) Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Comput 14(4):365–378
20. Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines, software, www.csie.ntu.edu.tw/~cjlin/libsvm
21. Chen G, Wang F, Zhang C (2009) Collaborative filtering using orthogonal nonnegative matrix tri-factorization. Inf Process Manag 45(3):368–379
22. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278
23. Cho J, Kwon K, Park Y (2007) Collaborative filtering using dual information sources. Intell Syst IEEE 22(3):30–38
24. Christakou C, Vrettos S, Stafylopatis A (2007) A hybrid movie recommender system based on neural networks. Int J Artif Intell Tools 16(05):771–792
25. de Campos LM, Fernández-Luna JM, Huete JF (2008) A collaborative recommender system based on probabilistic inference from fuzzy observations. Fuzzy Sets Syst 159(8):1554–1576
26. De Lathauwer L, De Moor B, Vandewalle J (2000) On the best rank-1 and rank-(R 1, R 2,., Rn) approximation of higher-order tensors. SIAM J Matrix Anal Appl 21(4):1324–1342
27. De Lathauwer L (2004) First-order perturbation analysis of the best rank-(R1, R2, R3) approximation in multilinear algebra. J Chem 18(1):2–11
28. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst TOIS 22(1):143–177
29. Destercke S (2012) A k-nearest neighbours method based on imprecise probabilities. Soft Comput 16(2):833–844
30. Drucker H, Shahrary B, Gibbon DC (2001) Relevance feedback using support vector machines. In: ICML, pp 122–129
31. Ferrari S, Bellocchio F, Piuri V, Borghese NA (2010) Multi-scale support vector regression. In: Neural Networks (IJCNN), The 2010 international joint conference on IEEE, pp 1–7
32. Gao M, Wu Z (2009) Personalized context-aware collaborative filtering based on neural network and slope one. In: cooperative design, visualization, and engineering. Springer, pp 109–116
33. Gedikli F, Jannach D (2013) Improving recommendation accuracy based on item-specific tag preferences. ACM Trans Intell Syst Technol TIST 4(1):11
34. Georgiou O, Tsapatsoulis N (2010) Improving the scalability of recommender systems by clustering using genetic algorithms. In: artificial neural networks-ICANN 2010. Springer, pp 442–449
35. Gong S, Ye H (2009) An item based collaborative filtering using bp neural networks prediction. In: industrial and information systems. IIS’09. International conference on, 2009. IEEE, pp 146–148
36. Grcar M, Fortuna B, MladeniF D, Grobelnik M (2006) kNN versus SVM in the collaborative filtering framework. In: data science and classification. Springer, pp 251–260
37. Gunawardana A, Meek C (2009) A unified approach to building hybrid recommender systems. In: Proceedings of the third ACM conference on Recommender systems. ACM, pp 117–124
38. Hanani U, Shapira B, Shoval P (2001) Information filtering: overview of issues, research and systems. User Model User Adapt Interact 11(3):203–259
39. Hayajneh MT, Hassan AM, Al-Wedyan F (2010) Monitoring defects of ceramic tiles using fuzzy subtractive clustering-based system identification method. Soft Comput 14(3):615–626
40. Stormer H, Werro N, Risch D (2006) Recommending products with a fuzzy classification. Europe, CollECTeR
41. Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 230–237
42. Igelnik B, Pao Y-H (1995) Estimation of size of hidden layer on basis of bound of generalization error. In: Proceedings of neural networks. IEEE international conference on, 1995. IEEE, pp 1923–1927
43. Jang J-S (1993) ANFIS: Adaptive-network-based fuzzy inference system. Syst Man Cybern IEEE Trans 23(3):665–685
44. Jannach D (2008) Finding preferred query relaxations in content-based recommenders. In: intelligent techniques and tools for novel system architectures. Springer, pp 81–97
45. Jannach D, Karakaya Z, Gedikli F (2012) Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM conference on electronic commerce. ACM, pp 674–689
46. Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press
47. Jeong B, Lee J, Cho H (2009) An iterative semi-explicit rating method for building collaborative recommender systems. Expert Syst Appl 36(3):6181–6186
48. Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. Springer, Berlin, pp 137–142
49. Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: Proceedings of the European conference on machine learning, Berlin
50. Kaufinan L, Rousseeuw PJ (1990) Finding groups in data: an introduction to Cluster analysis, Wiley
51. Kim H-N, Ji A-T, Ha I, Jo G-S (2010) Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electron Commer Res Appl 9(1):73–83
52. Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500
53. Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J (1997) GroupLens: applying collaborative filtering to Usenet news. Commun ACM 40(3):77–87
54. Lee M, Choi P, Woo Y (2002) A hybrid recommender system combining collaborative filtering with neural network. In adaptive hypermedia and adaptive web-based systems. Springer, Berlin, pp 531–534
55. Lee PY, Hui SC, Fong ACM (2002) Neural networks for web content filtering. Intell Syst IEEE 17(2):48–57
56. Lee Y-J, Mangasarian OL (2001) SSVM: a smooth support vector machine for classification. Comput Optim Appl 20(1):5–22
57. Leginus M, Zemaitis V (2011) Speeding up tensor based recommenders with clustered tag space and improving quality of recommendations with non-negative tensor factorization. Master’s thesis, Aalborg University
58. Lesaffre M, Leman M (2007) Using fuzzy logic to handle the users’ semantic descriptions in a music retrieval system. In: theoretical advances and applications of fuzzy logic and soft computing. Springer, pp 89–98
59. Li Q, Myaeng SH, Kim BM (2007) A probabilistic music recommender considering user opinions and audio features. Inf Process Manag 43(2):473–487
60. Li Q, Wang C, Geng G (2008) Improving personalized services in mobile commerce by a novel multicriteria rating approach. In: Proceedings of the 17th international conference on World Wide Web, pp 1235–1236
61. Linden G, Smith B, York J (2003) Amazon. com recommendations: item-to-item collaborative filtering. Int Comput IEEE 7(1):76–80
62. Liu L, Mehandjiev N, Xu D-L (2011) Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the fifth ACM conference on recommender systems. ACM, pp 77–84
63. Luo X, Xia Y, Zhu Q (2012) Incremental collaborative filtering recommender based on regularized matrix factorization. Knowl Based Syst 27:271–280
64. Manouselis N, Costopoulou C (2007) Experimental analysis of design choices in multiattribute utility collaborative filtering. Int J Pattern Recognit Artif Intell 21(02):311–331
65. Murphey YL, Luo Y (2002) Feature extraction for a multiple pattern classification neural network system. In: Proceedings of pattern recognition. 16th international conference on, 2002. IEEE, pp 220–223
66. Nauck D (1997) Neuro-fuzzy systems: review and prospects. In: Proceedings of fifth European congress on intelligent techniques and soft computing (EUFIT’97). pp 1044–1053
67. Nilashi M, Ibrahim O, Bagherifard K, Janahmadi N, Barisami M (2011c) Application of k-nearest neighbour predictor for classifying online customer trust. J Theor Appl Inf Technol 36(1):18–25
68. Nilashi M, Ibrahim OB (2013b) A model for detecting customer level intentions to purchase in B2C Websites using TOPSIS and fuzzy logic rule-based system. Arab J Sci Eng, pp 1–16
69. Nilashi M, Bagherifard K, Ibrahim O, Alizadeh H, Nojeem LA, Roozegar N (2013a) Collaborative filtering recommender systems. Res J Appl Sci Eng Technol 5(12):4168–4182
70. Nilashi M, Bagherifard K, Ibrahim O, Janahmadi N, Barisami M (2011a) An application expert system for evaluating effective factors on trust in B2C Websites. Engineering 3:7
71. Nilashi M, Fathian M, Gholamian MR, Ibrahim OB, Talebi A, Ithnin N (2011b) A comparative study of adaptive neuro fuzzy inferences system (ANFIS) and fuzzy inference system (FIS) approach for trust in B2C electronic commerce websites. JCIT 6(5):25–43
72. Nilashi M, Ibrahim O, Ithnin N (2014) Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowl Based Syst 60:82–101
73. Nilashi M, Ibrahim OB, Ithnin N (2014) Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Syst Appl 41(8):3879–3900
74. O’Connor P (2008) User-generated content and travel: a case study on tripadvisor. com. In: information and communication technologies in tourism 2008. Springer, pp 47–58
75. Ou G, Murphey YL (2007) Multi-class pattern classification using neural networks. Pattern Recognit 40(1):4–18
76. Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(7):10059–10072
77. Park Y-J, Chang K-N (2009) Individual and group behavior-based customer profile model for personalized product recommendation. Expert Syst Appl 36(2):1932–1939
78. Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5–6):393–408
79. Petrovic-Lazarevic S, Coghill K, Abraham A (2004) Neuro-fuzzy modelling in support of knowledge management in social regulation of access to cigarettes by minors. Knowl Based Syst 17(1):57–60
80. Pinto MA, Tanscheit R, Vellasco M (2012) Hybrid recommendation system based on collaborative filtering and fuzzy numbers. In: fuzzy systems (FUZZ-IEEE), IEEE international conference on, 2012. IEEE, pp 1–6
81. Postorino MN, Sarne GM (2011) A neural network hybrid recommender system. In: neural nets WIRN10. In: Proceedings of the 20th Italian workshop on neural nets, 2011. IOS Press, p 180
82. Rennie JD, Srebro N (2005) Fast maximum margin matrix factorization for collaborative prediction. In: Proceedings of the 22nd international conference on machine learning. ACM, pp 713–719
83. Sahoo N, Krishnan R, Duncan G, Callan J (2011) Research note-the halo effect in multicomponent ratings and its implications for recommender systems: the case of Yahoo!. Movies. Inf Syst Res 23(1):231–246
84. Sahoo N, Krishnan, R, Duncan, G, Callan JP (2006) Collaborative filtering with multi-component rating for recommender systems. In: Proceedings of the sixteenth workshop on information technologies and systems
85. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Application of dimensionality reduction in recommender system-a case study. DTIC document
86. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. ACM, pp 285–295
87. Schölkopf B, Smola A (2002) Learning with kernels. MIT Press, Cambridge
88. Sen S, Vig J, Riedl J (2009) Tagommenders: connecting users to items through tags. In: Proceedings of the 18th international conference on World wide web. ACM, pp 671–680
89. Si L, Jin R (2003) Flexible mixture model for collaborative filtering. In: Proceedings of the 20th international conference on machine learning. D.C, Washington, p 704
90. Silva C, Ribeiro B (2007) On text-based mining with active learning and background knowledge using SVM. Soft Comput 11(3):519–530
91. Sugeno M (1985) Industrial applications of fuzzy control. Elsevier
92. Sun J-T, Zeng H-J, Liu H, Lu Y, Chen Z (2005) CubeSVD: a novel approach to personalized web search. In: Proceedings of the 14th international conference on World Wide Web. ACM, pp 382–390
93. Symeonidis P, Nanopoulos A, Manolopoulos Y (2008a) Providing justifications in recommender systems. Syst Man Cybern Part A Syst Humans IEEE Trans 38(3):1262–1272
94. Symeonidis P, Nanopoulos A, Manolopoulos Y (2008) Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2008 ACM conference on recommender systems. ACM, pp 43–50
95. Symeonidis P, Ruxanda M, Nanopoulos A, Manolopoulos Y (2008) Ternary semantic analysis of social tags for personalized music recommendation. In: ISMIR’08: Proceedings of the 9th international conference on music information retrieval. Citeseer, pp 219–224
96. Tang TY, McCalla G (2009) The pedagogical value of papers: a collaborative-filtering based paper recommender. J Digit Inf 10(2)
97. Tsai C-F, Hung C (2012) Cluster ensembles in collaborative filtering recommendation. Appl Soft Comput 12(4):1417–1425
98. Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin
99. Vapnik V (1998) Statistical Learning Theory. Wiley, New York
100. Vapnik V, Golowich S, Smola A (1996) Support vector method for function approximation, regression estimation, and signal processing. Adv Neural Inf Process Syst 9:281–287
101. Vapnik V (1995) The nature of statistical learning theory. Springer, New York
102. Villalba SD, Cunningham P (2007) An evaluation of dimension reduction techniques for one-class classification. Artif Intell Rev 27(4):273–294
103. Xia Z, Dong Y, Xing G (2006) Support vector machines for collaborative filtering. In: Proceedings of the 44th annual Southeast regional conference. ACM, pp 169–174
104. Xu Y, Zhang L, Liu W (2006) Cubic analysis of social bookmarking for personalized recommendation. In: frontiers of WWW research and development-APWeb 2006. Springer, pp 733–738
105. Yager RR (2003) Fuzzy logic methods in recommender systems. Fuzzy Sets Syst 136(2):133–149
106. Yang Y, Liu X (1999) A re-examination of text categorization methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 42–49
107. Yazdani A, Ebrahimi T, Hoffmann U (2009) Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier. In: neural engineering. NER’09. 4th international IEEE/EMBS conference on, 2009. IEEE, pp 327–330
108. Zadeh LA (1965) Fuzzy Sets. Inf Control 8(3):338–353
109. Zhang F, Chang H-Y (2006) A collaborative filtering algorithm employing genetic clustering to ameliorate the scalability issue. In: e-Business Engineering. ICEBE’06. IEEE international conference on, 2006. IEEE, pp 331–338
110. Zhang T, Iyengar VS (2002) Recommender systems using linear classifiers. J Mach Learn Res 2:313–334
111. Zhang Z, Ye N (2011) Learning a tensor subspace for semi-supervised dimensionality reduction. Soft Comput 15(2):383–395
112. Zhou L, Lai KK, Yu L (2009) Credit scoring using support vector machines with direct search for parameters selection. Soft Comput 13(2):149–155