Application of Recommender System for Spending Habits Based Campaign Management †
Abstract
:1. Introduction
2. Method
2.1. Dataset and Processing
2.2. Research Model
3. Findings
- regParam is regularization that reduces overfitting,
- rank is the number of latent factors in the model,
- maxIter is the maximum number of iterations to run,
- alpha, is a parameter for implicit feedback that governs the baseline confidence in preference observations values.
4. Discussion and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Al Karim, R.; Habiba, W. Effects of CRM Components on Firm’s Competitive Advantage: A Case on Bangladesh Banking Industry. Manag. Res. 2020, 10, 1–7. [Google Scholar]
- Pokharel, B. Customer Relationship Management: Related Theories, Challenges and Application in Banking Sector. Bank. J. 1970, 1, 19–28. [Google Scholar] [CrossRef]
- Xu, C. Personal recommendation using a novel collaborative filtering algorithm in customer relationship management. Discret. Dyn. Nat. Soc. 2013, 2013. [Google Scholar] [CrossRef]
- Isinkaye, F.O.; Folajimi, Y.O.; Ojokoh, B.A. Recommendation systems: Principles, methods and evaluation. Egypt. Inform. J. 2015, 16, 261–273. [Google Scholar] [CrossRef]
- Gupta, S.; Dave, M. Improvised Collaborative Filtering for Recommendation System. Int. J. Innov. Technol. Explor. Eng. 2020, 9, 361–364. [Google Scholar] [CrossRef]
- Stratigi, M.; Li, X.; Stefanidis, K.; Zhang, Z. Ratings vs. Reviews in Recommender Systems: A Case Study on the Amazon Movies Dataset; Springer: Cham, Switzerland, 2019; Volume 3, ISBN 9783030302788. [Google Scholar]
- Srikanth, B.; Nagalakshmi, V. Songs Recommender System using Machine Learning Algorithm: SVD Algorithm. Int. J. Innov. Sci. Res. Tech. 2020, 5, 390–392. [Google Scholar]
- Kulkarni, I.; Gandhi, P.; Karlekar, P. Book Recommendation System Using Apache Spark. Int. J. Innov. Res. Comp. Comm. Eng. 2017, 7982–7987. [Google Scholar] [CrossRef]
- Agarwal, G.; Bahuguna, H.; Agarwal, A. Solving Cold-Start Problem in Recommender System Using User. Int. J. Emergig Tech. 2017, 8, 55–61. [Google Scholar]
- Dutta, S.; Kumar, B.S. Recommender System for Term Deposit Likelihood Prediction Using Cross-validated Neural Network. Preprints 2020, 19, 1–37. [Google Scholar] [CrossRef]
- Hernández-Nieves, E.; Hernández, G.; Gil-González, A.B.; Rodríguez-González, S.; Corchado, J.M. Fog computing architecture for personalized recommendation of banking products. Expert Syst. Appl. 2020, 140. [Google Scholar] [CrossRef]
- Aggarwal, C.C. Recommender Systems; Springer: Cham, Switzerland, 2017; ISBN 9783319296579. [Google Scholar]
- Gorakala, S.K. Building Recommendation Engines; Packt Publishing: Birmingham, UK, 2016; ISBN 978-1-78588-485-6. [Google Scholar]
- Johnson, C. Logistic matrix factorization for implicit feedback data. Adv. Neural Inf. Process. Syst. 2014, 27, 1–9. [Google Scholar]
- Hu, Y.; Park, F.; Koren, Y.; Volinsky, C.; Park, F. Collaborative Filtering for Implicit Feedback Datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 15–19 December 2008. [Google Scholar]
- Tasoulis, D.K.; Weston, D.J.; Adams, N.M.; Hand, D.J. Mining Information from Plastic Card Transaction Streams. In Proceedings of the in Computational Statistics: 18th Symposium (COMPSTAT 2008), Porto, Portugal, 24–29 August 2008; pp. 315–322. [Google Scholar]
- Apache Spark. Available online: https://spark.apache.org/docs/2.2.0/ml-collaborative-filtering.html (accessed on 13 August 2020).
MCG Code | Definition |
---|---|
MCG1 | Kids |
MCG2 | Other Payments (Dealers) |
MCG3 | Education |
MCG4 | Home |
MCG5 | Bill Payments |
MCG6 | Clothing and accessory |
MCG7 | Hobby and entertainment |
MCG8 | Supermarket |
MCG9 | Car and transportation |
MCG10 | Health and personal care |
MCG11 | Unclassified expenses |
MCG12 | Insurance |
MCG13 | Vacation and travel |
MCG14 | Tax and legal fees |
MCG15 | Restaurant payments |
MCG16 | Investment and savings |
Number of Latent Factors | Regularization | Alpha | MPR—10 Iterations | MPR—20 Iterations |
---|---|---|---|---|
8 | 0.05 | 10 | 0.504 | 0.485 |
8 | 0.05 | 20 | 0.519 | 0.489 |
8 | 0.01 | 10 | 0.525 | 0.522 |
8 | 0.01 | 20 | 0.540 | 0.513 |
8 | 0.02 | 10 | 0.556 | 0.521 |
8 | 0.02 | 20 | 0.536 | 0.580 |
10 | 0.05 | 10 | 0.390 | 0.424 |
10 | 0.05 | 20 | 0.390 | 0.403 |
10 | 0.01 | 10 | 0.378 | 0.418 |
10 | 0.01 | 20 | 0.380 | 0.413 |
10 | 0.02 | 10 | 0.357 | 0.400 |
10 | 0.02 | 20 | 0.365 | 0.376 |
12 | 0.05 | 10 | 0.325 | 0.370 |
12 | 0.05 | 20 | 0.325 | 0.339 |
12 | 0.01 | 10 | 0.330 | 0.374 |
12 | 0.01 | 20 | 0.319 | 0.346 |
12 | 0.02 | 10 | 0.361 | 0.403 |
12 | 0.02 | 20 | 0.356 | 0.386 |
16 | 0.05 | 10 | 0.271 | 0.227 |
16 | 0.05 | 20 | 0.308 | 0.257 |
16 | 0.01 | 10 | 0.263 | 0.213 |
16 | 0.01 | 20 | 0.312 | 0.248 |
16 | 0.02 | 10 | 0.265 | 0.217 |
16 | 0.02 | 20 | 0.318 | 0.241 |
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Kaya, T.S.; Gezer, M.; Gülseçen, S. Application of Recommender System for Spending Habits Based Campaign Management. Proceedings 2021, 74, 7. https://doi.org/10.3390/proceedings2021074007
Kaya TS, Gezer M, Gülseçen S. Application of Recommender System for Spending Habits Based Campaign Management. Proceedings. 2021; 74(1):7. https://doi.org/10.3390/proceedings2021074007
Chicago/Turabian StyleKaya, Tuğçe Süheyla, Murat Gezer, and Sevinç Gülseçen. 2021. "Application of Recommender System for Spending Habits Based Campaign Management" Proceedings 74, no. 1: 7. https://doi.org/10.3390/proceedings2021074007