Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning

Research Article
Open access

Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning

Rouyu Yu 1 , Dongming Li 2* , Yining Liu 3
  • 1 Oberlin College, Oberlin 44074, United States    
  • 2 School of Science in Mathematics and Applied Mathematics, Xiamen University Malaysia, Negeri Selangor,43900, Malaysia    
  • 3 Ulink College of Shanghai, Shanghai,201615, China    
  • *corresponding author MEC1909447@xmu.edu.my
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220545
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

In this paperwork, the Apriori algorithm can be used to describe the association between data in a database. This paper will use the demographic and financial information of the users in the data set to analyze the user profile as the basic information. This work will fit the model by optimizing the Apriori algorithm, generating a combination of two or more associated variables, and then using the decision tree to learn and output the final model. The main contribution of this paper is to explore the optimization of Apriori and the ef-fect of Apriori's combined model with the decision tree in predicting user buying behavior. Therefore, the conclusion can be drawn that in the sale of deposit products, if the custom-er has the above characteristics, such as no credit fault, no loan, housing loan, with a col-lege degree, marital status is single, and act as an administrator. The people mentioned above may be more interested in the product. In other words, it is more likely to sell suc-cessfully. In future work, in addition to deposit products, more customer buying infor-mation is highly needed to extend predictable results to broader anger.

Keywords:

Apriori, decision tree, association rule, methodology.

Yu,R.;Li,D.;Liu,Y. (2023). Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning. Applied and Computational Engineering,2,64-77.
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References

[1]. Walrave, M., Poels, K., Antheunis, M.L., Van den Broeck, E., van Noort, G. (2018) Like or dislike? Adolescents’ responses to personalized social network site advertising. Journal of Marketing Communications. 24: 599-616.

[2]. Semerádová, T., Weinlich, P. (2019) Computer estimation of customer similarity with Facebook lookalikes: Advantages and disadvantages of hyper-targeting. IEEE Access. 7: 153365-153377.

[3]. Bleier, A., Eisenbeiss, M. (2015) Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science. 34: 669-688.

[4]. Marquis, A. (n.d.) Definition of transparency advertising. https://smallbusiness.chron.com/definition-transparency-advertising-35939.html.

[5]. Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S. (2013) Toward the next generation of recommender systems: Applications and research challenges. In: Tsihrintzis, G.A., Virvou, M., Jain, L.C. (Eds.), Multimedia services in intelligent environments: Advances in recommender systems. Springer International Publishing, Heidelberg, pp. 81-98.

[6]. Burke, R. (2000) Knowledge-based recommender systems. Encyclopedia of Library and Information Systems. 69: 180–200.

[7]. Chung, R., Sundaram, D., Srinivasan, A. (2007) Integrated personal recommender systems. In: Proceedings of the ninth international conference on Electronic commerce. Minneapolis, MN, USA. pp. 65–74.

[8]. Quinlan, J.R. (1986) Induction of decision trees. Machine Learning. 1: 81-106.

[9]. Pazzani, M.J., Billsus, D. (2007) Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (Eds.), The adaptive web: Methods and strategies of web personalization. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 325-341.

[10]. Kim, J.W., Lee, B.H., Shaw, M.J., Chang, H.-L., Nelson, M. (2001) Application of decision-tree induction techniques to personalized advertisements on internet storefronts. International Journal of Electronic Commerce. 5: 45-62.

[11]. Agrawal, Rakesh, et al.(1998) Fast Algorithms for Mining Association Rules, Fast Algorithms for Mining Association Rules and Readings in Database Systems 3rd Ed.,97-102.

[12]. Moro, S., Cortez, P., Rita, P. (2014) A data-driven approach to predict the success of bank telemarketing. Decision Support Systems. 62: 22-31.

[13]. Agrawal, R., Srikant, R., (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp. 487-499.

[14]. Breiman, L., Friedman, J., Olshen, R., Stone, C. (2017) Classification and regression trees.

[15]. Chonyy. (2020) Apriori: Association rule mining in-depth. https://towardsdatascience.com/apriori-association-rule-mining-explanation-and-python-implementation-290b42afdfc6.

[16]. Freund, Y., Mason, L. (1999) The alternating decision tree learning algorithm. In: International Conference on Machine Learning. pp. 124-133.

Cite this article

Yu,R.;Li,D.;Liu,Y. (2023). Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning. Applied and Computational Engineering,2,64-77.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Walrave, M., Poels, K., Antheunis, M.L., Van den Broeck, E., van Noort, G. (2018) Like or dislike? Adolescents’ responses to personalized social network site advertising. Journal of Marketing Communications. 24: 599-616.

[2]. Semerádová, T., Weinlich, P. (2019) Computer estimation of customer similarity with Facebook lookalikes: Advantages and disadvantages of hyper-targeting. IEEE Access. 7: 153365-153377.

[3]. Bleier, A., Eisenbeiss, M. (2015) Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science. 34: 669-688.

[4]. Marquis, A. (n.d.) Definition of transparency advertising. https://smallbusiness.chron.com/definition-transparency-advertising-35939.html.

[5]. Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S. (2013) Toward the next generation of recommender systems: Applications and research challenges. In: Tsihrintzis, G.A., Virvou, M., Jain, L.C. (Eds.), Multimedia services in intelligent environments: Advances in recommender systems. Springer International Publishing, Heidelberg, pp. 81-98.

[6]. Burke, R. (2000) Knowledge-based recommender systems. Encyclopedia of Library and Information Systems. 69: 180–200.

[7]. Chung, R., Sundaram, D., Srinivasan, A. (2007) Integrated personal recommender systems. In: Proceedings of the ninth international conference on Electronic commerce. Minneapolis, MN, USA. pp. 65–74.

[8]. Quinlan, J.R. (1986) Induction of decision trees. Machine Learning. 1: 81-106.

[9]. Pazzani, M.J., Billsus, D. (2007) Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (Eds.), The adaptive web: Methods and strategies of web personalization. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 325-341.

[10]. Kim, J.W., Lee, B.H., Shaw, M.J., Chang, H.-L., Nelson, M. (2001) Application of decision-tree induction techniques to personalized advertisements on internet storefronts. International Journal of Electronic Commerce. 5: 45-62.

[11]. Agrawal, Rakesh, et al.(1998) Fast Algorithms for Mining Association Rules, Fast Algorithms for Mining Association Rules and Readings in Database Systems 3rd Ed.,97-102.

[12]. Moro, S., Cortez, P., Rita, P. (2014) A data-driven approach to predict the success of bank telemarketing. Decision Support Systems. 62: 22-31.

[13]. Agrawal, R., Srikant, R., (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp. 487-499.

[14]. Breiman, L., Friedman, J., Olshen, R., Stone, C. (2017) Classification and regression trees.

[15]. Chonyy. (2020) Apriori: Association rule mining in-depth. https://towardsdatascience.com/apriori-association-rule-mining-explanation-and-python-implementation-290b42afdfc6.

[16]. Freund, Y., Mason, L. (1999) The alternating decision tree learning algorithm. In: International Conference on Machine Learning. pp. 124-133.

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