Analysis of user behavior using hierarchical classification and fuzzy logic

Document Type : Research Paper

Authors

Department of Computer Engineering, Safadasht Branch, Islamic Azad University, Safadasht, Tehran, Iran

10.22075/ijnaa.2023.32461.4828

Abstract

In today's age, the Internet has become the most important source of information and its supply for many goods and services due to its ease of use, wide range and high speed. The unique characteristics of the Internet and its complete superiority over other markets have led many organizations to expand their services and products in the Internet markets. Therefore, the Internet environment has become a very competitive environment for organizations. In the process of buying and checking user and consumer behavior, goods or services will be exchanged, and these goods or services can be information, physical or virtual products, and even emotions. Many kinds of research have been conducted in the analysis of users' behavior, but these researches were not highly accurate, therefore, in this research, an attempt was made to provide a solution for recommending items based on users' past behaviors to improve the accuracy of past methods. This proposed method can eliminate the noise in the data by using fuzzy logic and hierarchical analysis which is used for the phase of selecting the effective features and also works on more effective data to increase the accuracy. The tree proposed in this research has the lowest possible height and this causes the computational overhead to be greatly reduced. The proposed method was compared with several competing methods and the results indicate the superiority of the proposed method over the compared methods.

Keywords

[1] P. Covington, J. Adams, and E. Sargin, deep neural networks for YouTube recommendations, Proc. 10th ACM Conf. Recomm. Syst., 2016, pp. 191-198.
[2] R. Danesh Qalich Khani, M. Hakak, and A.A. Farhani, A model for measuring the direct and indirect impact of business intelligence with the partial mediating role of empowerment on organizational agility (Case study: Tehran engineering system organization and Atka organization), Int. Res. Conf. Sci. Engin., 2016. [In Persian]
[3] Gh. El-Hadad, D. Shawky, and A. Badawi, Adaptive learning guidance system (ALGS), arXiv preprint arXiv: 1911.06812 (2019).
[4] B.L. Golden and Q. Wang, An alternative measure of consistency, B. L. Golden, A. Wasil & P.T. Harker (eds.), Analytic Hierarchy Process: Applications and Studies, 1990, pp. 68–81.
[5] N. Jalaliyoon, N.A. Bakar, and H. Taherdoost, Accomplishment of critical success factor in organization; using analytic hierarchy process, Int. Jo. Acad. Res. Manag. 1 (2012), no. 1, 1–9.
[6] E.L. Jose, AHP-express: A simplified version of the analytical hierarchy process method, Methods X 7 (2020), 1–11.
[7] M. Krejnus, J. Stofkova, K.R. Stofkova, and V. Binasova, The use of the DEA method for measuring the efficiency of electronic public administration as part of the digitization of the economy and society, Appl. Sci. 13 (2023), no. 6, 36–72.
[8] M.C. Lee, H.W. Wang, and H.Y. Wang, A method of performance evaluation by using the analytic network process and balanced scorecard, Int. Conf. Converg. Inf. Technol., IEEE, 2007, pp. 235–240.
[9] L.C. Lin and G.P. Sharp, Quantitative and qualitative indices for the plant layout evaluation problem, Eur. J. Oper. Res. 116 (2000), no. 1, 100–117.
[10] J. McAuley, Ch. Targett, Q. Shi, and A. van den Hengel, Image-based recommendations on styles and substitutes, Proc. 38th Int. ACM SIGIR Conf. Res. Dev. Inf. Retrieval, 2015.
[11] L. Ren and W. Wang, An SVM-based collaborative filtering approach for Top-N web services recommendation, Future Gen. Comput. Syst. 78 (2018), no. 4, 531–543.
[12] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, BPR: Bayesian personalized ranking from implicit feedback, Proc. 25th Conf. Uncertain. Artiff. Intell., 2009, 452–461.
[13] V. Rokhi Nesab, Z. Ebrahimi and A. Rajaei, the role of data mining in electronic business, Nat. Comput. Conf. Inf. Technol. Artif. Intell. Appl., Ahvaz, 2016.
[14] T. L. Saaty, The Analytic Hierarchy Process, McGraw-Hill, New York, 1980.
[15] H. Sharma and S. Kumar, A survey on decision tree algorithms of classification in data mining, Int. J. Sci. Res. 5 (2016), no. 4, 2094–2097.
[16] M. Stankovic, P. Gladovic and V. Popovic, Determining the importance of the criteria of traffic accessibility using fuzzy AHP and rough AHP method, Decis. Mak.: Appl. Manag. Engin. 2 (2019), no. 1, 86–104.
[17] J. Sun, H. Zhao, S. Mu, and Z. Li, Purchasing behavior analysis based on customer’s data portrait model, IEEE 43rd Ann. Comput. Software Appl. Conf., 2019.
[18] R.H. Tsiotsou, Value creation in tourism through active tourist engagement: A framework for online reviews, Women’s Voices Tourism Res. 12 (2021), no. 1, 1–14.
[19] J. Wang, P. Huang, H. Zhao, Z. Zhang, B. Zhao, and D.L. Lee, Billion-scale commodity embedding for e-commerce recommendation in Alibaba, Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discov. Data Min., 2018.
[20] A.L. Zadeh, Fuzzy logic, Computer 21 (1998), no. 4, 83–93.
[21] G. Zhou, J. Zhao, T. He, and W. Wu, An empirical study of topic-sensitive probabilistic model for expert finding in question answer communities, Knowledge-Based Syst. 66 (2014), no. 4, 136–145.

Articles in Press, Corrected Proof
Available Online from 18 February 2024
  • Receive Date: 27 October 2023
  • Revise Date: 11 December 2023
  • Accept Date: 23 December 2023