Collaborative filtering recommendation based on user personality
Zhichao Quan · 2013
Traditional collaborative filtering recommendation system put emphasis on data but ignores the users. The recommendation of the similarity analysis emphasizing too much from the data perspective lacks depth analysis of the users without regarding the similarity from the users' perspective. In this paper, user personality is introduced to improve the user model, and two personality-based collaborative filtering recommendations are proposed: one is to compute user similarity from the user personality perspective and select nearest neighbor, and then generates recommendation; another is based on the personality-item rating matrix, and then make recommendation to the target users. These two ideas can well make up for the inadequacies of the current collaborative filtering recommendation system. In the meantime, the experiment result shows that user personality-based collaborative filtering approach performs better than existing ones.