Implementing a Hybrid Recommendation System to Personalize Customer Experience in E-Commerce Domain

Abstract

With the rapid use of the internet, a huge amount of data is generated over the network with every passing second. On the other hand, the user demands information that is relevant to his personal search. The processing of such huge data is a challenging task. To serve the user with his specific required information, there is a need for an information retrieval mechanism that can process this large data. A recommendation system is such required technology that retrieves information in order to improve users’ access and thereby recommending items that are relevant to his explicitly mentioned behavior and preferences. The recommendation algorithm analyzes the huge dataset and focuses to recommend accurate content to the user. There are several recommendation systems in use today, some popular among them are Netflix, YouTube, Tinder, and Amazon. In this article, discussion is done on various types of recommendation systems, issues of recommendation systems, and use cases of widely used recommendation engines and their potential benefits. The work introduced in this paper is an integration approach of domain-specific and item-based recommendation system. The performance of the proposed algorithm is measured on evaluation metrics precision and recall. Experimental results prove that the approach introduced in this paper performed well as compared to existing methods.