Systematic approach for cold start issues in recommendations system

Abstract

Personalized recommendation systems are widely used with the development of electronic commerce. Recommender systems provide the users a list of recommendations they might prefer or supply predictions, on how much the user might prefer each item. The environment we use for recommending the books with high ratings is Mahout, and for massive storage we go for BigData analysis. This provides a recommendation system by analyzing engrossment of the user and features of the books. With the gradual increase of the customer and product, the traditional collaborative filtering suffers accuracy, sparsity, scalability and cold start problem. In suchcircumstances, where new items are added and are rated only by few users, here a cold start issue arises. To solve these problems, we propose a recommendation system in mahout, where the items without enough ratings will be compared with the users preference list to recommend the books to the users. By combining content and collaborative filtering techniques, we reduce the cold start problems upto some extent. Thus we recommend books with high ratings, based on the preferences of the users.