Abstract
Recommendation systems have popular methods that help users generate predictions for a product and create product lists based on users’ feedback. The output of such systems can increase customer satisfaction by producing accurate recommendations. However, the success of predictions depends on the level of personalization of user preference data. It is possible to increase the personalization level by collecting more detailed customer data. Multi-criteria recommender systems using the user-item matrix which users evaluate in terms of more than one criterion, are introduced to achieve such a goal. Besides the accuracy of predictions, vulnerability against shilling attacks is an important challenge for recommender systems. However, there is no study in the literature that deals with the robustness of multi-criteria top-n recommendation methods. Hence, in this study, robustness analysis of three different item-based multi-criteria top-n recommendation methods is performed. We use new and existing evaluation criteria for the effectiveness of attacks, an attack model adapted to multi-criteria systems, and an experimental design that included target and power item selection. According to the experimental outcomes based on a real-world dataset, it can be said that multi-criteria top-n algorithms are vulnerable to manipulations.
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References
Adomavicius, G.; Manouselis, N.; Kwon, Y.: Multi-criteria recommender systems. In: Recommender Systems Handbook, pp. 769–803. (2011)
Batmaz, Z.; Yilmazel, B.; Kaleli, C.: Shilling attack detection in binary data: a classification approach. J. Ambient. Intell. Humaniz. Comput. 11(6), 2601–2611 (2020)
Bradley, K.; Smyth, B.: Improving recommendation diversity. In: Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland, pp. 85–94. (2001)
Burke, R.; Mobasher, B.; Bhaumik, R.: Limited knowledge shilling attacks in collaborative filtering systems. In: Proceedings of 3rd International Workshop on Intelligent Techniques for Web Personalization (ITWP 2005), 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 17–24. (2005)
Burke, R.; Mobasher, B.; Zabicki, R.; Bhaumik, R.: Identifying attack models for secure recommendation. In: Beyond Personalization: A Workshop on the Next Generation of Recommender Systems, pp. 347–361. (2005)
Burke, R.; O’Mahony, M.P.; Hurley, N.J.: Robust collaborative recommendation. In: Recommender Systems Handbook. Springer, pp. 961–995. (2015)
Chen, K.; Chan, P.P.; Zhang, F.; Li, Q.: Shilling attack based on item popularity and rated item correlation against collaborative filtering. Int. J. Mach. Learn. Cybern. 10(7), 1833–1845 (2019)
Cheng, Z.; Hurley, N.: Robust collaborative recommendation by least trimmed squares matrix factorization. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence. vol. 2. IEEE, pp. 105–112. (2010)
Cho, Y.H.; Kim, J.K.; Kim, S.H.: A personalized recommender system based on web usage mining and decision tree induction. Expert Syst. Appl. 23(3), 329–342 (2002)
Deldjoo, Y.; Di Noia, T.; Di Sciascio, E.; Merra, F.A.: A Regression Framework to Interpret the Robustness of Recommender Systems Against Shilling Attacks (2021)
Dev, A.V.; Mohan, A.: Recommendation system for big data applications based on set similarity of user preferences. In: 2016 International Conference on Next Generation Intelligent Systems (ICNGIS). IEEE, pp. 1–6. (2016)
Gaikwad, R.S.; Udmale, S.S.; Sambhe, V.K.: E-commerce recommendation system using improved probabilistic model. In: Information and Communication Technology for Sustainable Development. Springer, pp. 277–284. (2018)
Ghazanfar, MA.; Prugel-Bennett, A.: A scalable, accurate hybrid recommender system. In: 2010 Third International Conference on Knowledge Discovery and Data Mining. IEEE, pp. 94–98. (2010)
Gunes, I.; Kaleli, C.; Bilge, A.; Polat, H.: Shilling attacks against recommender systems: a comprehensive survey. Artif. Intell. Rev. 42(4), 767–799 (2014). https://doi.org/10.1007/s10462-012-9364-9
Hurley, N.J.; O’Mahony, M.P.; Silvestre, G.C.: Attacking recommender systems: a cost-benefit analysis. IEEE Intell. Syst. 22(3), 64–68 (2007)
Hwangbo, H.; Kim, Y.S.; Cha, K.J.: Recommendation system development for fashion retail e-commerce. Electron. Commer. Res. Appl. 28, 94–101 (2018)
Jiang, L.; Cheng, Y.; Yang, L.; Li, J.; Yan, H.; Wang, X.: A trust-based collaborative filtering algorithm for E-commerce recommendation system. J. Ambient. Intell. Humaniz. Comput. 10(8), 3023–3034 (2019)
Kaminskas, M.; Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7(1), 1–42 (2016)
Katarya, R.; Verma, O.P.: A collaborative recommender system enhanced with particle swarm optimization technique. Multimedia Tools Appl. 75(15), 9225–9239 (2016)
Kaur, P.; Goel, S.; Shilling attack models in recommender system. In: International Conference on Inventive Computation Technologies (ICICT). vol. 2. IEEE 2016, pp. 1–5. (2016)
Kaya, T.; Kaleli, C.: A novel top-n recommendation method for multi-criteria collaborative filtering. Expert Syst. Appl. (2022). https://doi.org/10.1016/j.eswa.2022.116695
Kotkov, D.; Wang, S.; Veijalainen, J.: A survey of serendipity in recommender systems. Knowl.-Based Syst. 111, 180–192 (2016)
Kou, G.; Xu, Y.; Peng, Y.; Shen, F.; Chen, Y.; Chang, K.; et al.: Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decis. Support Syst. 140, 113429 (2021)
Kou, G.; Olgu Akdeniz, Ö.; Dinçer, H.; Yüksel, S.: Fintech investments in European banks: a hybrid IT2 fuzzy multidimensional decision-making approach. Financial Innov. 7(1), 1–28 (2021)
Kou, G.; Yüksel, S.; Dinçer, H.: Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects. Appl. Energy 311, 118680 (2022)
Kumar, V.; Pujari, A.K.; Sahu, S.K.; Kagita, V.R.; Padmanabhan, V.: Collaborative filtering using multiple binary maximum margin matrix factorizations. Inf. Sci. 380, 1–11 (2017)
Kumar, M.A.; Singh, Y.; Siwach, V.; Sehrawat, H.: Impact analysis of profile injection attacks in recommender system. Inf. Technol. Ind. 9(1), 472–478 (2021)
Kumari, T.; Bedi, P.: A comprehensive study of shilling attacks in recommender systems. Int. J. Comput. Sci. Issues (IJCSI). 14(4), 44 (2017)
Lakiotaki, K.; Matsatsinis, N.F.; Tsoukias, A.: Multicriteria user modeling in recommender systems. IEEE Intell. Syst. 26(2), 64–76 (2011)
Lam, SK.; Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th international conference on World Wide Web, pp. 393–402. (2004)
Mehta, H.; Bhatia, SK.; Bedi, P.; Dixit, VS.: Collaborative personalized web recommender system using entropy based similarity measure. arXiv preprint arXiv:1201.4210. (2012)
Mobasher, B.; Burke, R.; Williams, C.; Bhaumik, R.: Analysis and detection of segment-focused attacks against collaborative recommendation. In: International Workshop on Knowledge Discovery on the Web. Springer, pp. 96–118. (2005)
Mobasher, B.; Dai, H.; Luo, T.; Nakagawa, M.: Improving the effectiveness of collaborative filtering on anonymous web usage data. In: Proceedings of the IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization (ITWP01), pp. 53–61. (2001)
Mobasher, B.; Burke, R.; Bhaumik, R.; Sandvig, J.J.: Attacks and remedies in collaborative recommendation. IEEE Intell. Syst. 22(3), 56–63 (2007)
Mobasher, B.; Burke, R.; Bhaumik, R.; Williams, C.: Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Trans. TOIT. 7(4), 23-es (2007)
Nadi, S.; Saraee, M.H.; Bagheri, A.; et al.: A hybrid recommender system for dynamic web users. Int. J. Multimedia Image Process. 1(1), 3–8 (2011)
Nasraoui, O.; Petenes, C.: An intelligent web recommendation engine based on fuzzy approximate reasoning. In: The 12th IEEE International Conference on Fuzzy Systems, FUZZ’03. vol. 2. IEEE, pp. 1116–1121. (2013)
O’Mahony, M.P.; Hurley, N.J.; Silvestre, G.C.: Attacking recommender systems: the cost of promotion. In: Proceedings of the workshop on recommender systems. In: Conjunction with the 17th European Conference on Artificial Intelligence, Riva del Garda, Trentino, Italy, pp. 24–28. (2006)
O’Mahony, M.P.; Hurley, N.J.; Silvestre, G.C.: Detecting noise in recommender system databases. In: Proceedings of the 11th International Conference on Intelligent User Interfaces, pp. 109–115. (2006)
O’Mahony, M.P.; Hurley, N.J.; Silvestre, G.C.: Recommender systems: attack types and strategies. In: AAAI, pp. 334–339. (2005)
O’Mahony, M.; Hurley, N.; Kushmerick, N.; Silvestre, G.: Collaborative recommendation: a robustness analysis. ACM Trans. IOIT 4(4), 344–377 (2004)
Peng, M.C.; Wang, R.Y.: Evaluating wedding banquet halls using a novel multiple-criteria decision-making model. Adv. Manag. Appl. Econ. 7(5), 13 (2017)
Seminario, CE.: Accuracy and robustness impacts of power user attacks on collaborative recommender systems. In: Proceedings of the 7th ACM conference on Recommender systems, pp. 447–450. (2013)
Seminario, C.E.; Wilson, D.C.: Nuking item-based collaborative recommenders with power items and multiple targets. In: The Twenty-Ninth International Flairs Conference (2016)
Seminario, C.E.; Wilson, D.C.: Assessing impacts of a power user attack on a matrix factorization collaborative recommender system. In: The Twenty-Seventh International Flairs Conference (2014)
Seminario, C.E.; Wilson, D.C.: Attacking item-based recommender systems with power items. In: Proceedings of the 8th ACM Conference on Recommender systems, pp. 57–64. (2014)
Turk, A.M.; Bilge, A.: Robustness analysis of multi-criteria collaborative filtering algorithms against shilling attacks. Expert Syst. Appl. 115, 386–402 (2019)
Türkoğlu T. Çoklu ölçüt oy değerleri üzerinden veri madenciliği. Anadolu Üniversitesi. (2016)
Wang, Q.; Yuan, X.; Sun, M.: Collaborative filtering recommendation algorithm based on hybrid user model. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery. vol. 4. IEEE, pp. 1985–1990. (2010)
Wang, F.H.; Shao, H.M.: Effective personalized recommendation based on time-framed navigation clustering and association mining. Expert Syst. Appl. 27(3), 365–377 (2004)
Williams, C.; Mobasher, B.: Profile injection attack detection for securing collaborative recommender systems. DePaul University CTI Technical Report, pp. 1–47. (2006)
Wilson, D.C.; Seminario, C.E.: When power users attack: assessing impacts in collaborative recommender systems. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 427–430. (2013)
Yalcin, E.; Bilge, A.: Binary multicriteria collaborative filtering. Turkish J. Electri. Eng. Comput. Sci. 28(6), 3419–3437 (2020)
Zeng, X.; Wu, B.; Shi, J.; Liu, C.; Guo, Q.: Parallelization of latent group model for group recommendation algorithm. In: 2016 IEEE First International Conference on Data Science in Cyberspace (DSC). IEEE, pp. 80–89. (2016)
Zha, Q.; Kou, G.; Zhang, H.; Liang, H.; Chen, X.; Li, C.C.; et al.: Opinion dynamics in finance and business: a literature review and research opportunities. Financial Innov. 6(1), 1–22 (2020)
Zhang, F.; Reverse bandwagon profile inject attack against recommender systems. In: Second International Symposium on Computational Intelligence and Design. vol. 1. IEEE 2009, pp. 15–18. (2009)
Zhang, L.: The definition of novelty in recommendation system. J. Eng. Sci. Technol. Rev. 6(3), 141–145 (2013)
Zhou, B.; Hui, SC.; Chang, K.: An intelligent recommender system using sequential web access patterns. In: IEEE Conference on Cybernetics and Intelligent Systems, 2004. vol. 1. IEEE, pp. 393–398. (2004)
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This study was supported by Eskisehir Technical University Scientific Research Project Commission under the grant no: 20DRP034.
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Kaya, T.T., Kaleli, C. Robustness Analysis of Multi-Criteria Top-n Collaborative Recommender System. Arab J Sci Eng 48, 10189–10212 (2023). https://doi.org/10.1007/s13369-022-07568-w
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DOI: https://doi.org/10.1007/s13369-022-07568-w