Parallel Multi-View Graph Matrix Completion for Large Input Matrix

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 informat...

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Veröffentlicht in:2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) S. 0337 - 0341
Hauptverfasser: Koohi, Arezou, Homayoun, Houman
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2019
Schlagworte:
Matrix decomposition
multi-threaded programming
multi-view graph
parallel computing
Predictive models
Program processors
Programming
Recommender systems
Sparse matrices
Training
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Zusammenfassung: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.
DOI:10.1109/CCWC.2019.8666532