A New Approach for Book Recommendation Using Opinion Leader Mining

Recommendation systems (RSs) are used by different e-commerce sites like Amazon, eBay, etc., for suggesting relevant recommendations based upon users’ preferences or items purchased by people of similar interests. Recommendation systems help the users to identify the items which may be worth to buy. This is referred to as top-N recommendations. So, the ultimate goal of RS is to find out which item or product is more relevant as per user preference. In this paper, our main focus is to elaborate the recommendation process with follow-up of book recommender systems in the existing works and challenges associated with them. In order to deal with one of the recommendation challenges, we proposed book recommendation approach based on identification of opinion leaders in detected communities of social networks by using information related to their interests, preferences, age and online available attributes on social networks. This approach can be helpful in solving user cold-start problem, i.e. generating recommendations of books to new user in the absence of availability of purchase history. The same approach can be used for solving an item cold-start problem also, if none of the users had rated it till now.