A recommendation engine is a type of data filtering technology that uses machine learning techniques to provide the most relevant recommendations to a particular user or client. It operates by searching for patterns in client behavior data, which may be collected explicitly or implicitly. It first records a client's area of interest and then uses that information to enable product recommendations for customers who appear tobe shopping. For example: An e-commerce website won't know anything about a visitor if they are completely new to it. So how would the positioning strategy advocate the product to the consumer in this situation? One practicalsolution may be to suggest the item that is in great demand, or the one that is popular. Another practical option is to recommend the product that will likely bring more profit to the company. Recommendation engines can be implemented by using 3 strategies: - Collaborative filtering (focuses on collecting and analyzing data about user behavior, preferences, and activities to predict what a person would like based on how they are similar to other users.), content-based filtering (which works on the principle that if you like one thing, you'll like this other thing, too) and hybrid models.

Keywords : Recommendation System,Collaborative Filtering Approach, And Content- Based Filtering Method.