  Recommendation algorithm plays an important role in recommendation system
(RS), which predicts users' interests and preferences for some given items
based on their known information. Recently, a recommendation algorithm based on
the graph Laplacian regularization was proposed, which treats the prediction
problem of the recommendation system as a reconstruction issue of small samples
of the graph signal under the same graph model. Such a technique takes into
account both known and unknown labeled samples information, thereby obtaining
good prediction accuracy. However, when the data size is large, solving the
reconstruction model is computationally expensive even with an approximate
strategy. In this paper, we propose an equivalent reconstruction model that can
be solved exactly with extremely low computational cost. Finally, a final
prediction algorithm is proposed. We find in the experiments that the proposed
method significantly reduces the computational cost while maintaining a good
prediction accuracy.
