  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.
