Book Recommender System using Improved Collaborative Filtering

Abstract

In the day-to-day rapidly growing internet world, where the number of choices and data are abundant, there is an essential need to filter, prioritize and efficiently provide relevant information to the users in order to reduce the problem of information overload. Recommender solve this problem by searching through large volume of dynamically generated information to supply users with personalized content and services. Collaborative filtering is one of the best known and most extensive techniques in the recommendation system. Its basic idea is to predict which items the user would be interested in on the basis of their preferences. Recommendation systems using collaborative filtering can provide accurate prediction when sufficient data is provided, as this technique is based on the preference of the user. However, their precision is often comparable to increasingly confused and computationally costly calculations. To improve the execution time and accuracy of the prediction problem, this paper proposes the user preference based improved collaborative filtering approach. The system provides efficient book recommendation to the user when compared to other state of art techniques.