Collaborative Filtering for Book Recommendation System

Collaborative filtering is one of the most important techniques in the market nowadays. It is prevalent in almost every aspect of the internet, in e-commerce, music, books, social media, advertising, etc., as it greatly grasps the needs of the user and provides a comfortable platform for the user to find what they like without searching. This method has a few drawbacks; one of them being, it is based only on the explicit feedback given by the user in the form of a rating. The real needs of a user are also demonstrated by various implicit indicators such as views, read later lists, etc. This paper proposes and compares various techniques to include implicit feedback into the recommendation system. The paper attempts to assign explicit ratings to users depending on the implicit feedback given by users to specific books using various algorithms and thus, increasing the number of entries available in the table.