International Journal of Computer Science Issues

More than a traditional journal...

Research on evaluating recommender systems shows that algorithms in this area are still deficient in prediction accuracy but recent works prove that modeling with temporal dynamics improves the degree of recommendation accuracy. Recommendations are invariably based on similarities of users and/or items in the user-item matrix of a system, user profiles, and rating information which presumes the presence of users or items in the matrix. In Social Media the matrix is takes a different form that may include a user-user or user-attributes. There is limited work focused on the temporal absence as an indicator of preference or concept drift: and hence a factor for inclusion in the recommender algorithms and models either to improve accuracy or to enhance user interaction in social based recommendations. This paper defines temporal absence in the context of recommender systems and verifies, through examination of the Netflix Prize data, the extent of temporal absence and the significance of such information in future research and improvement of recommendation algorithms.