Binary multicriteria collaborative filtering

Collaborative filtering is specialized in suggesting appropriate products and services to the users concerning personal characteristics and past preferences without requiring any effort of users. It might be more efficient to collect preferences of users based on multiple subcriteria of products and services. For this purpose, researchers propose multicriteria recommender systems that are convenient for more accurate and useful evaluation of items. In such systems, it might be preferable to collect binary ratings instead of numerical ones due to the large number of subcriteria. However, there is a gap in the literature to satisfy a binary preferences-based multicriteria recommender system. In this study, the applicability of multicriteria recommender systems based on binary ratings is investigated. Firstly, recommendations for users on the overall criterion are produced by employing naïve Bayes classifier. In order to improve the quality of recommendations, user- and item-based similarity models are proposed enabling the formation of more successful neighborhoods. Such models are further improved by integrating a concordance measure between overall preference and subcriteria ratings, which helps to provide more personalized and meaningful similarities among users. Finally, a hybrid model is proposed employing user- and item-based models together and real data-based experimental outcomes demonstrate that the quality of estimated binary referrals is improved statistically significantly.

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[1] Recker Mimi M, Andrew W. Supporting “word-of-mouth” social networks through collaborative information filtering. Journal of Interactive Learning Research 2003; 14 (1): 79-98.
[2] Herlocker J, Konstan JA, Riedl J. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval 2002; 5 (4): 287-310. doi: 10.1023/A:1020443909834
[3] Kaleli C, Polat H. P2P collaborative filtering with privacy. Turkish Journal of Electrical Engineering & Computer Sciences 2010; 18 (1): 101-116. doi: 10.3906/elk-0808-21
[4] Adomavicius G, Kwon Y. Multi-criteria recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor P (editors). Recommender Systems Handbook. Boston, MA, USA: Springer, 2015, pp. 847-880.
[5] Jannach D, Karakaya Z, Gedikli F. Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM Conference on Electronic Commerce; Valencia, Spain; 2012. pp. 674-689. doi: 10.1145/2229012.2229065
[6] Bilge A, Polat H. Improving privacy-preserving NBC-based recommendations by preprocessing. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology; Toronto, ON, Canada; 2010. pp. 143–147. doi: 10.1109/WI-IAT.2010.109
[7] Miyahara K, Pazzani MJ. Collaborative filtering with the simple Bayesian classifier. In: Pacific Rim International Conference on Artificial Intelligence; Melbourne, Australia; 2000. pp. 679-689.
[8] Adomavicius G, Kwon Y. New recommendation techniques for multi-criteria rating systems. IEEE Intelligent Systems 2007; 22 (3): 48-55. doi: 10.1109/MIS.2007.58
[9] Kaur G, Ratnoo S. Adaptive genetic algorithm for feature weighting in multi-criteria recommender systems. Pertanika Journal of Science & Technology 2019; 27 (1): 1-25.
[10] Fan J, Xu L. A robust multi-criteria recommendation approach with preference-based similarity and support vector machine. In: 10th International Symposium on Neural Networks; Dalian, China; 2013. pp. 385-394. doi: 10.1007/978-3-642-39068-5_47
[11] Hassan M, Hamada M. A neural networks approach for improving the accuracy of multi-criteria recommender systems. Applied Sciences 2017; 7 (9): 868. doi: 10.3390/app7090868
[12] Hamada M, Hassan M. Artificial neural networks and particle swarm optimization algorithms for preference prediction in multi-criteria recommender systems. Informatics 2018; 5 (2): 25. doi: 10.3390/informatics5020025
[13] Farokhi N, Vahid M, Nilashi M, Ibrahim O. A multi-criteria recommender system for tourism using fuzzy approach. Journal of Soft Computing and Decision Support Systems 2016; 3 (4): 19-29.
[14] Kant V, Jhalani T, Dwivedi P. Enhanced multi-criteria recommender system based on fuzzy Bayesian approach. Multimedia Tools and Applications 2018; 77: 1-19. doi: 10.1007/s11042-017-4924-2
[15] Majumder G, Dwivedi P, Kant V. Matrix factorization and regression-based approach for multi-criteria recommender system. In: International Conference on Information and Communication Technology for Intelligent Systems; Ahmedabad, India; 2018. pp. 103-110. doi: 10.1007/978-3-319-63673-3_13
[16] Batmaz Z, Kaleli C. AE-MCCF: an autoencoder-based multi-criteria recommendation algorithm. Arabian Journal for Science and Engineering 2019; 44 (11): 9235-9247. doi: 10.1007/s13369-019-03946-z
[17] Manouselis N, Costopoulou C. Experimental analysis of design choices in multiattribute utility collaborative filtering. International Journal of Pattern Recognition and Artificial Intelligence 2007; 21 (02): 311-331. doi: 10.1142/S021800140700548X
[18] Hu Y, Chiu Y, Liao Y, Li Q. A fuzzy similarity measure for collaborative filtering using nonadditive grey relational analysis. The Journal of Grey System 2015; 27 (2): 93-104.
[19] Naak A, Hage H, Aimeur E. A multi-criteria collaborative filtering approach for research paper recommendation in papyres. In: International Conference on E-Technologies; Ottawa, ON, Canada; 2009. pp. 25-39. doi: 10.1007/978- 3-642-01187-0_3
[20] Hu Y, Chiu Y, Tsai J. Establishing grey criteria similarity measures for multi-criteria recommender systems. Journal of Grey System 2018; 30 (1): 194-207.
[21] Liu L, Mehandjiev N, Xu D. Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the Fifth ACM Conference on Recommender Systems; Chicago, IL, USA; 2011. pp. 77-84. doi: 10.1145/2043932.2043950
[22] Lakiotaki K, Tsafarakis S, Matsatsinis N. UTA-Rec: a recommender system based on multiple criteria analysis. In: Proceedings of the 2008 ACM Conference on Recommender Systems; Lausanne, Switzerland; 2008. pp. 219-226. doi: 10.1145/1454008.1454043
[23] Hu Y. Neighborhood-based collaborative filtering using grey relational analysis. Journal of Grey System 2014; 26 (1): 99-114.
[24] Bilge A, Kaleli C. A multi-criteria item-based collaborative filtering framework. In: 11th International Joint Conference on Computer Science and Software Engineering (JCSSE); Chon Buri, Thailand; 2014. pp. 18-22. doi: 10.1109/JCSSE.2014.6841835
[25] Hwang W. Assessing new correlation-based collaborative filtering approaches for binary market basket data. Electronic Commerce Research and Applications 2018; 29: 12-18. doi: 10.1016/j.elerap.2018.03.002
[26] Verstrepen K, Bhaduriy K, Cule B, Goethals B. Collaborative filtering for binary, positive-only data. ACM SIGKDD Explorations Newsletter 2018; 19 (1): 1-21. doi: 10.1145/3137597.3137599
[27] Miyahara K, Pazzani MJ. Improvement of collaborative filtering with the simple Bayesian classifier. Information Processing Society of Japan 2002; 43 (11): 1-20.
[28] Ng AY, Jordan MI. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. In: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic; Vancouver, BC, Canada; 2001. pp. 841-848.
[29] Choi SS, Cha SH, Tappert CC. A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics 2010; 8 (1): 43-48.
[30] Lathia N, Hailes S, Capra L. Private distributed collaborative filtering using estimated concordance measures. In: Proceedings of the 2007 ACM Conference on Recommender Systems; Minneapolis, MN, USA; 2007. pp. 1-8. doi: 10.1145/1297231.1297233