Book Recommendation System through content based and collaborative filtering method
The online recommendation system has become a trend. Now a days rather than going out and buying items for themselves, reason being, online recommendation provides an easier and quicker way to buy items and transactions are also quick when it is done online. Recommended systems are powerful new technology and it helps users to find items which they want to buy. A recommendation system is broadly used to recommend products to the end users that are most appropriate. Online book selling Web sites now a days is competing with each other by considering many attributes. A recommendation system is one of the strongest tools to increase profits and retaining buyer. The existing systems lead to extraction of irrelevant information and lead to lack of user satisfaction. This paper presents Book Recommendation System (BRS) based on combined features of content based filtering (CBF) collaborative filtering (CF) and association rule mining to produce efficient and effective recommendation. For this we are proposing a hybrid algorithm in which we combine two or more algorithms, so it helps the recommendation system to recommend the book based on the buyer's interest.
2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)