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Found 6 papers, 5 papers with code
Knowledge-refined Denoising Network for Robust Recommendation
Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability.
Self-Guided Learning to Denoise for Robust Recommendation
Besides, our method can automatically switch its learning phase at the memorization point from memorization to self-guided learning, and select clean and informative memorized data via a novel adaptive denoising scheduler to improve the robustness.
HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
Furthermore, we propose a dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns.
Ranked #1 on
Recommendation Systems
on Alibaba-iFashion
MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation
Inspired by the success of meta-learning on scarce training samples, we propose a novel meta-learning based framework called MetaKG, which encompasses a collaborative-aware meta learner and a knowledge-aware meta learner, to capture meta users' preference and entities' knowledge for cold-start recommendations.
Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories
Trajectory similarity computation has drawn massive attention, as it is core functionality in a wide range of applications such as ride-sharing, traffic analysis, and social recommendation.
Hformer: Hybrid CNN-Transformer for Fringe Order Prediction in Phase Unwrapping of Fringe Projection
Our work opens an alternative way to deep learning based phase unwrapping methods, which are dominated by CNN in fringe projection 3D measurement.
