  Contrastive Learning (CL)-based recommender systems have gained prominence in
the context of Heterogeneous Graph (HG) due to their capacity to enhance the
consistency of representations across different views. However, existing
frameworks often neglect the fact that user-item interactions within HG are
governed by diverse latent intents (e.g., brand preferences or demographic
characteristics of item audiences), which are pivotal in capturing fine-grained
relations. The exploration of these underlying intents, particularly through
the lens of meta-paths in HGs, presents us with two principal challenges: i)
How to integrate CL with intents; ii) How to mitigate noise from
meta-path-driven intents.
  To address these challenges, we propose an innovative framework termed
Intent-guided Heterogeneous Graph Contrastive Learning (IHGCL), which designed
to enhance CL-based recommendation by capturing the intents contained within
meta-paths. Specifically, the IHGCL framework includes: i) a meta-path-based
Dual Contrastive Learning (DCL) approach to effectively integrate intents into
the recommendation, constructing intent-intent contrast and intent-interaction
contrast; ii) a Bottlenecked AutoEncoder (BAE) that combines mask propagation
with the information bottleneck principle to significantly reduce noise
perturbations introduced by meta-paths. Empirical evaluations conducted across
six distinct datasets demonstrate the superior performance of our IHGCL
framework relative to conventional baseline methods. Our model implementation
is available at https://github.com/wangyu0627/IHGCL.
