Matrix and tensor completion techniques have proven useful in many applications such as recommender systems, image/video restoration, and web search. We explore the idea of using external information in completing missing values in tensors. In this work, we present a framework that employs side information as kernel matrices for tensor factorization. We apply our framework to problems of recommender systems and video restoration and show that our framework effectively deals with the cold-start problem.