Hybrid Attribute and Personality based Recommender System for Book Recommendation

In recent years, with the rapid increases of books, finding relevant books has been a problem. For that, people might need their peers' opinion to complete this task. The problem is that relevant books can be gained only if there are other users or peers have same interests with them. Otherwise, they will never get relevant books. Recommender systems can be a solution for that problem. They work on finding relevant items based on other users' experience. Although research on recommender system increases, there is still not much research that considers user personality in recommender systems, even though personal preferences are really important these days. This paper discusses our research on a hybrid-based method that combines attribute-based and user personality-based methods for book recommender system. The attribute-based method has been implemented previously. In our research, we have implemented the MSV-MSL (Most Similar Visited Material to the Most Similar Learner) method, since it is the best method among hybrid attribute-based methods. The personality factor is used to find the similarity between users when creating neighborhood relationships. The method is tested using Book-crossing and Amazon Review on book category datasets. Our experiment shows that the combined method that considers user personality gives a better result than those without user personality on Book-crossing dataset. In contrary, it resulted in a lower performance on Amazon Review dataset. It can be concluded that user personality consideration can give a better result in a certain condition depending on the dataset itself and the usage proportion.