Recommender system by grasping individual preference and influence from other users

We propose a recommendation method that considers the user´s individual preference and influence from other users in social media. This method predicts the user´s individual preference and influence from other users by applying the probability of divergence from random-selection based on a statistical hypothesis test as a form of modified content-based filtering. We evaluated the proposed method by focusing on the rate at which items that have recommended tags are contained among all items. The proposed method is shown to have higher accuracy than traditional content-based filtering. It is especially effective when some percentage of the items have recommendation tags.