IMPACT OF E-COMMERCE RECOMMENDATION SYSTEM ON CONSUMER BEHAVIOR

Abstract

Recommendation system is an information-filtering system which helps the consumer get best product recommendation's which matches their interests. The first ever known solution was the computer librarian Grundy, who took interviews of users about what they like and their preferences and then recommended books to them on the basis of information provided. This approach is outdated today as it's a time consuming process and technology advancements have made it easier and a seamless process but at that time it was a paradigm shift. As we've seen in previous years, e-commerce is rapidly expanding, so it's critical to keep improving how customers interact with the platform and what they find useful, as well as understanding the actions they take before making a purchase decision, in order to build a better platform and plan for increasing customer satisfaction and sales. Customers are becoming more knowledgeable about the strategies utilised by marketers to convince them in this digital era. As a result, establishing a win-win scenario will benefit the organization's long-term growth, which is more significant than short-term profits. Because every customer is different, we must find features that work for the majority of consumers when they engage with the recommendation system while making a purchase choice. Knowing what works well for customers and leads to a purchase decision allows a company to make adjustments that will benefit both the company and the customers by increasing sales and allowing them to buy what they want. Different aspects connected to recommendation systems are being investigated in order to determine how to run various parts in order to make it a more customer-friendly system that leads to increased sales.