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Found 3 papers, 2 papers with code
REST: Debiased Social Recommendation via Reconstructing Exposure Strategies
By explicitly Reconstructing Exposure STrategies (REST in short), we formalize the recommendation problem as the counterfactual reasoning and propose the debiased social recommendation method.
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations
To achieve this, we firstly formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences.
Graph Domain Adaptation: A Generative View
Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules.
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