Online Product Recommendation System

Abstract

In today’s digital world, day to day number of customer drastically increases in online shopping. There are a huge amount of customer’s information and product’s information available to maintain. This information is very useful to increases sales revenue. The system is required which has filter to filter out these information and efficiently provide relevant recommendation in order to reduce the problem of information overload and increased Internet traffic. Online recommendation systems act as a virtual agent which help user to take up right product from the abundant amount of products purchasable on the e-commerce site by providing an effective recommendation. Reviews or ratings provided by user, used to build up product profile and user navigation is used to build up user profiles, both are used for recommending products that best matches with the user’s interest. Recommendation systems identify recommendations automatically for individual buyers based on past purchases and searches, product rating and on other users behavior. This paper includes advantages and limitations of recommendation system and detail description of all techniques which are used for recommendation with its pros and cons. User-based and Item-based techniques are very popular for recommendation, we have discuss its algorithm with complexity analysis and quantitative analysis.