KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019

A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

KDD CUP 2019 Baseline

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

52nd solution of KDD Cup 2019 Regular ML track

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.