using personality Enhanced item-based recommender system for cold start

Recommender systems which are a subset of web mining are currently one of the widely applied aspects of data mining. Recommender systems help users more easily and quickly find products that they truly prefer amidst the enormous volume of information available to them. Since providing a user-friendly environment is one of the most important things in e-commerce, this branch of web mining is popular among researchers. In this paper we propose a method that combines personality traits into the traditional rating-based similarity computation in the framework of item-based recommender systems with the motivation to make good recommendations for new users who have rated a few items. We further compare our method with pure traditional ratings-based similarity and other similar systems in several experimental conditions. Experimental results shows that the proposed algorithm provides more advantages in terms of improving recommendation quality and it can efficiently address the new user problem.