Graph-based Alignment and Uniformity for Recommendation

• URL: http://arxiv.org/abs/2308.09292v1
• Date: Fri, 18 Aug 2023 04:33:36 GMT
• Title: Graph-based Alignment and Uniformity for Recommendation
• Authors: Liangwei Yang, Zhiwei Liu, Chen Wang, Mingdai Yang, Xiaolong Liu, Jing Ma, Philip S. Yu
• Abstract summary: Collaborative filtering-based recommender systems rely on learning representations for users and items to predict preferences accurately. We propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph. Experiments on four datasets show that GraphAU significantly alleviates the sparsity issue and achieves state-of-the-art performance.
• Score: 45.40299300441636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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