A new prediction method for recommendation system based on sampling reconstruction of signal on graph

Recommendation technology is widely used in various e-commerce platforms. Accurately predicting user's preference is the most important goal of recommendation technology. One of the core difficulties of recommendation technology that the rating matrices are seriously sparse. However, the unknown entries in the rating matrix actually contain a lot of useful information for prediction, which are usually discarded in traditional methods. Based on the idea of semi-supervised learning, this paper models the recommendation problem as a signal reconstruction problem on a graph. The new model utilizes both the information of the unlabeled samples and the location information, and thus achieves an excellent predictive performance. Meanwhile, to reduce the computational complexity a strategy is designed skillfully to approximately solve the model. Experimental results shows that the proposed method significantly outperforms the reference methods in predictive accuracy and is robust to the diversity of data sets. (C) 2020 Elsevier Ltd. All rights reserved.