# KDD 2022

![last modify](https://img.shields.io/static/v1?label=last%20modify\&message=2022-10-13%2001%3A56%3A19\&color=yellowgreen\&style=flat-square)

## Workshops

* 表示学习、不平衡学习：Deep learning practice and theory for high-dimensional, sparse, and imbalanced data
* 知识图谱：International Workshop on Knowledge Graphs: Open Knowledge Network
* 时间序列：Workshop on Mining and Learning from Time Series -- Deep Forecasting: Models, Interpretability, and Applications

## 论文

* Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
  * 知识图谱；KGTransformer；
  * 面向复杂逻辑查询的知识图谱Transformer预训练。
* AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System
  * 搜索；粗排；双塔模型优化；神经网络框架搜索(Neural Architecture Search)
* Practical Counterfactual Policy Learning for Top-K Recommendations
  * 数据优化；
  * 问题：对于训练机器学习模型，一项关键任务是通过收集的反馈（例如，评分、点击）来构建训练数据。 然而，从理论和实际经验中可以发现，收集的反馈中选择偏差会导致训练得到的模型有偏，从而导致训练结果是不是最优策略。
* Applying Deep Learning Based Probabilistic Forecasting to Food Preparation Time for On-Demand Delivery Service
  * 配送时间预测；
  * 应用概率估计刻画订单出餐时间的不确定性（区别于点估计）；提出了S-QL损失函数（基于S-CRPS）
* A Framework for Multi-stage Bonus Allocation in meal delivery Platform
  * 应用；物流配送相关；
  * 多阶段送餐奖励框架；优化无人接单问题；
* DDEN:A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts Network
  * 应用；异构排序；