Multifaceted recommender systems methods: A review

Abstract

The advent of the internet age has led to the availability of overwhelming choices of products and services. Now, it’s a responsibility of service provider to filter, prioritize and efficiently deliver only relevant information to the customer. The Internet has served as a platform for businesses which can produce better results as compared to the traditional way of selling goods and services. Amazon,Snapdeal, Flipkart are such companies that have already flourished online. These businesses use recommender systems to increase their profitability.

Recommender Systems not only uses a customer’s buying habits but also involve knowledge of user reviews, ratings, correlation, etc. Recommendation systems algorithms have been developed over time aimed at improving the efficiency of user recommendations which can easily convert into a purchase. This paper will mainly highlight the types of recommendation systems with their scope for improvements. Different systems follow different principles for providing user recommendations based on their defined prediction technique. An overview of such systems is showcased in the paper. The paper proposes a model based on a multifaceted recommendation technique involving a combination of multiple approaches at the same time.

Subject Classification:

• 68P99

Keywords:

• Collaborative filtering systems
• Content-based filtering
• Hybrid recommendation systems
• Study of Recommendation system