KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019
-
Updated
Aug 5, 2020 - Python
KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019
A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"
Implementation for the paper "K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters,", which has been accepted by KDD'2019 as an ORAL paper, in the Research Track.
A prototype version of our submitted paper: Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives.
Repository for KDD-Cup 2019 with Baidu. Big Data Science practical course @ LMU
Code for the KDD 2019 workshop paper. Attention mechanism for distribution regression.
code for the KDD 2019 workshop paper https://arxiv.org/abs/1904.10583. Kernel mean embedding for distribution regression.
To associate your repository with the kdd2019 topic, visit your repo's landing page and select "manage topics."