Comparison of Machine Learning Techniques for Recommender Systems for Financial Data

Machine Learning Techniques for Recommender Systems

Authors

  • Divya G Nair
  • K Muralidharan The Maharaja Sayajirao University of Baroda, Vadodara

Keywords:

Financial Analysis, Recommender systems, Collaborative Filtering, Cosine Similarity, Singular value decomposition

Abstract

Recommender Systems are one of the most successful and widespread application of machine learning technologies in business. These are the software tools used to give suggestions to users on the basis of their requirements. Increase in number of options: be it number of online websites or number of products, it has become difficult for the customer to choose from a wide range of products. Today there is no system available for banks to provide financial advisory services to the customers and offer them relevant products as per their preference before they approach the bank. Like any other industries, financial service rarely has any like, feedback and browsing history to record ratings of services. So it becomes a challenge to build recommender systems for financial services. In this research paper, authors propose a collaborative filtering technique to recommend various products to the customer in order to increase the product per customer (PPC) ratio of bank. The advantage of these recommender systems is that it provides better suggestion to the customer based on his needs/requirements for his/her savings, expenditure and investments.

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Published

2020-12-01

How to Cite

1.
Nair DG, Muralidharan K. Comparison of Machine Learning Techniques for Recommender Systems for Financial Data: Machine Learning Techniques for Recommender Systems. JKSA [Internet]. 2020 Dec. 1 [cited 2022 May 25];31(1):68-84. Available from: https://ojs.ksa.org.in/index.php/JKSA/article/view/33

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Section

Articles