Topology-aware Debiased Self-supervised Graph Learning for Recommendation

• URL: http://arxiv.org/abs/2310.15858v1
• Date: Tue, 24 Oct 2023 14:16:19 GMT
• Title: Topology-aware Debiased Self-supervised Graph Learning for Recommendation
• Authors: Lei Han and Hui Yan and Zhicheng Qiao
• Abstract summary: We propose Topology-aware De Self-supervised Graph Learning ( TDSGL) for recommendation. TDSGL constructs contrastive pairs according to the semantic similarity between users (items) Our results show that the proposed model outperforms the state-of-the-art models significantly on three public datasets.
• Score: 6.893289671937124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recommendation, graph-based Collaborative Filtering (CF) methods mitigate the data sparsity by introducing Graph Contrastive Learning (GCL). However, the random negative sampling strategy in these GCL-based CF models neglects the semantic structure of users (items), which not only introduces false negatives (negatives that are similar to anchor user (item)) but also ignores the potential positive samples. To tackle the above issues, we propose Topology-aware Debiased Self-supervised Graph Learning (TDSGL) for recommendation, which constructs contrastive pairs according to the semantic similarity between users (items). Specifically, since the original user-item interaction data commendably reflects the purchasing intent of users and certain characteristics of items, we calculate the semantic similarity between users (items) on interaction data. Then, given a user (item), we construct its negative pairs by selecting users (items) which embed different semantic structures to ensure the semantic difference between the given user (item) and its negatives. Moreover, for a user (item), we design a feature extraction module that converts other semantically similar users (items) into an auxiliary positive sample to acquire a more informative representation. Experimental results show that the proposed model outperforms the state-of-the-art models significantly on three public datasets. Our model implementation codes are available at https://github.com/malajikuai/TDSGL.

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