Graph-based Alignment and Uniformity for Recommendation
This work addresses sparsity in recommender systems, which is a domain-specific problem, and appears incremental as it builds on existing hypersphere representation learning approaches.
The paper tackles the sparsity issue in collaborative filtering-based recommender systems by proposing GraphAU, a graph-based method that uses high-order connectivities to improve representation learning, achieving state-of-the-art performance on four datasets.
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.