Financial planning recommendation system using content-based collaborative and demographic filtering

Abstract

The one stop to all problems is the Internet. But finding relevant information is difficult. The interest of the user lies in different forms of information content such as images, text, audio, or videos. The recommendation system is a process of information filtering that helps users to find better products, financial plans, and other related information by personalizing the suggestions. There are different recommendations techniques such as collaborative filtering, demographic recommendation, knowledge-based recommendations, content-based recommendation, and utility-based recommendation system. These techniques fail to eliminate the drawbacks such as data sparsity, new user cold start problem, new item cold start problem, overspecialization, and shilling attacks. In today’s generation, saving income is very important. In this work, a recommendation system for financial planning is proposed. Here, the idea is to modify the recommendation process to improve the recommendations in the best possible way. The above-mentioned drawbacks are eliminated using hybrid approach. In the hybrid approach, the techniques of collaborative filtering, i.e., user–user and item–item similarity along with demographic filtering, are combined. The experimental result is evaluated using performance metrics precision and recall. An ROC curve is used for evaluating the system.