Parallel Multi-View Graph Matrix Completion for Large Input Matrix - Koohi, Arezou; Homayoun, Houman (2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), 2019)
We propose a method for parallel multi-view graph matrix completion for the prediction of ratings in recommender systems. The missing ratings are computed based on both the similarity matrix in addition to a rating matrix. The rating matrix is sparse and some items might not have any rating information available. The similarity matrix can be calculated from different item attributes available from ecommerce websites. As the input matrix becomes large, the need for more computationally efficient matrix completion increases. The main contribution of this paper is to show speed-up in calculating the missing ratings by using multi-threaded programming. Simulation results are based on the large input matrix and show reduction in RMSE for the case of cold start prediction.