An Equivalent Graph Reconstruction Model and its Application in Recommendation Prediction

TL;DR

This paper introduces an efficient equivalent graph reconstruction model for recommendation systems that reduces computational costs significantly while maintaining high prediction accuracy.

Contribution

The paper proposes a novel equivalent reconstruction model that is computationally efficient and suitable for large-scale recommendation systems.

Findings

Reduces computational cost compared to existing methods

Maintains high prediction accuracy in experiments

Applicable to large-scale recommendation data

Abstract

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.