Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image pro- cessing and data gathering to classification and recommender systems. Recently, deep neural networks have been proposed as la- tent factor models for matrix completion and have achieved state-of-the-art performance. Nevertheless, a major problem with existing neural-network-based models is their limited capabilities to extend to samples unavailable at the training stage. In this paper, we propose a deep two-branch neural network model for matrix completion. The proposed model not only inherits the predictive power of neural networks, but is also capable of extending to partially observed samples outside the training set, without the need of retraining or fine-tuning. Experimental studies on popular movie rating datasets prove the effectiveness of our model compared to the state of the art, in terms of both accuracy and extendability.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Number of pages5
StateAccepted/In press - 15 Apr 2018

ID: 37200083