ارائه سیستم توصیه گر مبتنی بر آنالیز عقاید جهت ارائه خدمات شخصی سازی شده بانکی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت فناوری اطلاعات ( کسب و کار هوشمند)، دانشگاه علوم تحقیقات تهران

2 دانشیار گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

3 عضو هیئت علمی (استاد تمام) دانشگاه علوم تحقیقات تهران

4 عضو هیئت علمی (دانشیار) دانشگاه علوم تحقیقات تهران

چکیده

حفظ مشتری یک مسئله مهم برای بانک ها می باشد. مسئله حاضر در حوزه یادگیری ماشین و مباحث آماری با تمرکز بر مشکل پیش بینی صحیح نیازهای مشتری و پاسخگویی به آن در محیط پویای بانک است. از آنجا که به ندرت حرکت موثری با بهره گیری از اقدامات شخصی سازی شده برای بهبود نرخ نگهداری مشتری انجام شده، با این حال، این تصمیمات حداقل به عنوان شناسایی صحیح مشتریان در معرض خطر ریزش بسیار مهم است. تصمیم گیری برای اقداماتی جهت ارائه خدمات خاص به مشتریان و شخصی سازی به طور معمول به مدیران منتهی می شود که آن ها تنها می توانند بر دانش خود تکیه کنند. با بررسی ادبیات علمی دربارهCRM ، این تحقیق مدلی را که می تواند برای تولید فعالیت های حفظ مشتری و بازاریابی در بانکداری شخصی استفاده شود، ارائه می دهد که شامل بررسی تحلیلی مشتریان و تولید اقداماتی در راستای نگهداری آن هاست و در آن تحلیلگران همچنین مجموعه ای از اقدامات شخصی سازی شده را برای حفظ مشتریان با استفاده از یکی از رویکردهای ارائه شده در این مقاله، با استفاده از یک سیستم توصیه گر با بهره گیری از عقاید و تجارب مشتریان ارائه خوهند داد.

کلیدواژه‌ها


عنوان مقاله [English]

Provide an Opinion Analysis-Based Recommender System for Personalized Personal Banking Services

نویسندگان [English]

  • Mehregan Ghobakhloo 1
  • Ali Rajabzadeh Ghatari 2
  • Abbas Toloie Eshlaghy 3
  • Mahmood Alborzi 4
1 PhD student in Information Technology Management, Azad University, Research Sciences
2 (Associate Professor) of Tarbiat Modares University,
3 Faculty Member (Full Professor) of Science and Research Branch,Islamic Azad University
4 Faculty Member (Associate Professor) of Science and Research Branch,Islamic Azad University
چکیده [English]

Customer retention is an important issue for banks. This issue in the field of machine learning and statistical issues is focusing on the problem of accurately predicting customer needs and responding to it in the bank’s dynamic environment. Since rarely effective action has been taken by considering personalized actions to improve customer retention rates, but these decisions are at least as important as the proper identification of endangered customers. Deciding about what to do to provide specific and personalized services for customers typically leads to managers who can only rely on their knowledge. By reviewing the literature on CRM, this research provides a model that can be used to generate customer retention and marketing activities in personal banking, which includes analyzing clients and producing actions to maintain them, and analysts also set Using personalized actions to maintain customers using one of the approaches presented in this article, they are using an recommender system utilizing the opinion and experiences of customers.

کلیدواژه‌ها [English]

  • customer opinion
  • customer experience
  • Recommender System
  • personal banking
  • Personalization
[1]    Albadvi A., Chaharsooghi K., and Esfahanipour A (2007) "Decision making       in stock trading: An application of PROMETHEE", European Journal of   Operational Research, 673-683.

[2]    Asosheh, A., Bagherpour, S. Yahyapour, N. (2008). "Extended acceptance models for recommender system adaption, case of retail and banking service in Iran", WSEAS transactions on business and economics, 5(5): 189-200.

[3]    Bahrinizadeh, M., Esmailpour, M., Kaboutari, J (2017). "Evaluating and Ranking the Quality Components of E-Services Affecting Customer Satisfaction and Intent", Journal of Business Intelligence Management Studies, vol. 22, pp. 49-74.

[4]    Borhani Zarandi, S., Niknafas, Mohammadi (2013). "Opinion mining in product review by using emotional vocabulary", 2nd national conference on Industrial & Systems Engineering, Islamic Azad University of Najafabad.

[5     Chao Ma ., Xun Liang (2015). "Online mining in unstructured financial information", An empirical study in bulletin news.

[6]    Chen Y.-L. and Cheng L.-C (2008) "A novel collaborative filtering approach for recommending ranked items", Expert systems with applications, vol. 34, pp. 2396-2405.

[7]    Cornelis C., Lu J., Guo X., and Zhang G (2007) "One-and-only item recommendation with fuzzy logic techniques", Information sciences, vol. doi:10.1016/j.ins.2007.07.001.

[8]    Fasanghari M., Montazer Gh (2010), "Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation", Expert Systems with Applications, 37(9), 6138–6147,

[9]    Islam M., Habib M. (2015), "A data mining approach to predict prospective business sectors for lending in Retail banking using decision tree", arXiv preprint arXiv:1504.02018.

[10] Jin, J., P. Ji and R. Gu (2016) "Identifying comparative customer requirements from product online reviews for competitor analysis." Engineering Applications of Artificial Intelligence 49: 61-73.

[11] Kangas, S. (2002) "Collaborative filtering and recommendation systems. in: VTT information technology". Espoo: VTT.

[12] Karimian, S., Karegar, M. (2012) "Quantifying the emotional tendency of Persian-language customer comments on the features of the product on the Web", 1st international conference of web research, Knowledge and Culture University.

[13] Karimi Alavije, M., Askari, S. & Parasite, S. (2015) "Intelligent Online Store: User Behavior Analysis based Recommender System", Journal of Information Technology Management. 7(2): 385-406.

[14] Kim, Y. S., Yum, B. J., Song, J. & Kim, S. M. (2005), "Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites", Expert Systems with Applications. 28(1): 381-393.

[15] Kompan, M. & Bieliková, M. (2010), "Content-based news recommendation", International Conference on Electronic commerce and web technologies (EC-Web 2010), University of Deusto, Bilbao, 30 August - 3 September 2010.

 [16] Liu, B., M. Hu and J. Cheng (2005) "Opinion observer: analyzing and comparing opinions on the Web", Proceedings of the 14th international conference on World Wide Web. Chiba, Japan, ACM: 342-351.

[17] Li Y., Lu L., and Xuefeng L (2005), "A hybrid collaborative filtering method for multiple-interests and multiplecontent recommendation in E-Commerce", Expert systems with applications, vol. 28, pp. 67-77.

[18] Marrese-Taylor, E., J. D. Velásquez and F. Bravo-Marquez (2014), "A novel deterministic approach for aspectbased opinion mining in tourism products reviews", Expert Systems with Applications 41(17): 7764-7775.

[19] Martín-Guerrero, J. D. & Lisboa, P. J. & Soria-Olivas, E. & Palomares, A. & Balaguer, E. (2007), "An approach based on the Adaptive Resonance Theory for analyzing the viability of recommender systems in a citizen Web portal", Expert Systems with Applications, 33(3): 743-753.

[20] Miles, M.B.& Hubermn, A.M. (2017), "Qualitative Data Analysis" – A Sourcebook of New Methods, California, Sage.

[21] Rouhani S., Zandvakili R., Ansari M (2018), " Design and Implementation of a Tag-oriented Recommender System Based on Deep Neural Networks", Journal of Modern Research in Decision Making. Vol. (3) 2, 155-174.

 [22] Sohrabi B., Raeesi Vanani I., Zareh Mirkabad F (2016), " Designing a Recommender System for Optimizing and Managing Bank Facilities through the Utilization of Clustering and Classification Algorithms", Journal of Modern Research in Decision Making. Vol. (1) 2, 53-76.

[23] Soleimani-Roozbahani F., Rajabzadeh Ghatari A., Radfar R (2019) "Knowledge discovery from a more than a decade studies on healthcare Big Data systems: a scientometrics study", vol. 6, pages.8, Journal of Big Data. doi: https://doi.org/10.1186/s40537-018-0167-y

[24]  Taymouri asl Y., Jokar A (2015), "Provide a model of market orientation in the Iranian banking industry using the Delphi method", Journal of Management Research in Decision Making. Vol. (19)1, 45-67.

[25] Wang, Z., Sun, L., Zhu, W., Yang, S., Li, H. & Wu, D. (2013), "Joint social and content recommendation for user-generated videos in online social network"" IEEE Transactions on Multimedia, 15(3): 698-709.

[26] Xiaoming YANG Peng TIAN Zhen ZHANG, (2019)" A Comparative Study on Several National Customer Satisfaction Indices (CSI)" Aetna School of Management,Shanghai Jiao Tong University, Shanghai, P.R.China, ,p2

[27] Yang X., Chen J., Hao P., Wang Y. J (2015), "Application of clustering for customer segmentation in private banking", Seventh International Conference on Digital Image Processing, 96311Z; doi:10.1117/12.2197182.

[28] Yin, D. & Hong, L. & Davison, B. D (2011), "Structural link analysis and prediction in microblogs", Proceedings of the 20th ACM international conference on Information and knowledge management. Glasgow, 24-28.

[29]  Zahra S., Ghazanfar M. A., Khalid A., Azam M. A., Naeem U., Prugel-Bennett A. (2015), "Novel centroid selection approaches for KMeans-clustering based recommender systems", Information Sciences, 320, 156-189. doi:

[30] Zarei A (2015), "Developing a Structural Model for Customer Churn in Governmental Banks: Case of Semnan Governmental Banks", Journal of Management Research in Decision Making. Vol. (21)1, 151-176.