A Hybrid Model for Book Recommendation
Rohit Darekar, Karan Dayma, Rohan Parabh, Swapnali Kurhade · 2018
Recommendations are an important part of various e-commerce and seller websites. Recommending a product to the users has helped a lot of these companies in predicting the interests on that product. Recommendation are personalized display of products after narrowing down the thousands of items in the inventory to a useful few. A Book Recommendation Engine can be helpful to many people as they will be suggested what book to read next. These can be learned from the user's choices over time and a ‘taste’ profile can be created by it. Existing approaches for Recommending involves selecting from two of the popular approaches i.e Collaborative learning and Content Based learning. User Collaborative learning works by finding similar user with the taste of target user and by recommending it according to the choice of a similar user while Content based-learning involves actually having the ‘knowledge’ of the item and then recommending the item to the target user based on the user's ‘taste’ profile. Since both these works well separately they can be used simultaneously making a hybrid learning and give the user more accurate results since they are combined together.