Amplify Graph Learning for Recommendation via Sparsity Completion

TL;DR

This paper introduces AGL-SC, a novel graph learning framework that enhances recommendation systems by completing and amplifying sparse graph structures using higher-order interaction features and variational inference.

Contribution

The paper proposes a new sparsity completion method for graph learning in recommendation systems, integrating higher-order features with variational inference for improved graph structure enhancement.

Findings

AGL-SC outperforms state-of-the-art methods on four real-world datasets.

The framework effectively mines higher-order interaction features.

Enhanced graph structures lead to better recommendation performance.

Abstract

Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preference edges, which significantly reduces the performance of recommendations. In this paper, we study how to enhance the graph structure for CF more effectively, thereby optimizing the representation of graph nodes. Previous works introduced matrix completion techniques into CF, proposing the use of either stochastic completion methods or superficial structure completion to address this issue. However, most of these approaches employ random numerical filling that lack control over noise perturbations and limit the in-depth exploration of higher-order interaction features of nodes, resulting in biased graph representations. In this paper, we propose an Amplify Graph Learning framework based on Sparsity Completion (called AGL-SC). First, we utilize graph neural network to mine direct interaction features between user and item nodes, which are used as the inputs of the encoder. Second, we design a factorization-based method to mine higher-order interaction features. These features serve as perturbation factors in the latent space of the hidden layer to facilitate generative enhancement. Finally, by employing the variational inference, the above multi-order features are integrated to implement the completion and enhancement of missing graph structures. We conducted benchmark and strategy experiments on four real-world datasets related to recommendation tasks. The experimental results demonstrate that AGL-SC significantly outperforms the state-of-the-art methods.