Graph-based Alignment and Uniformity for Recommendation

TL;DR

This paper introduces GraphAU, a graph-based method that improves representation learning in collaborative filtering recommender systems by explicitly considering high-order connectivities, leading to better handling of data sparsity and state-of-the-art results.

Contribution

GraphAU is a novel approach that leverages high-order graph connectivities and layer-wise alignment pooling to enhance recommendation accuracy.

Findings

Significantly alleviates data sparsity issues.

Achieves state-of-the-art performance on four datasets.

Effectively models high-order user-item relationships.

Abstract

Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to its desirable properties, such as alignment and uniformity. However, the sparsity issue arises when it encounters RecSys. To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph. GraphAU aligns the user/item embedding to the dense vector representations of high-order neighbors using a neighborhood aggregator, eliminating the need to compute the burdensome alignment to high-order neighborhoods individually. To address the discrepancy in alignment losses, GraphAU includes a layer-wise alignment pooling module to integrate alignment losses layer-wise. Experiments on four datasets show that GraphAU significantly alleviates the sparsity issue and achieves state-of-the-art performance. We open-source GraphAU at https://github.com/YangLiangwei/GraphAU.