博客
关于我
强烈建议你试试无所不能的chatGPT,快点击我
WSDM 2020关于深度推荐系统与CTR预估工业界必读的论文
阅读量:2091 次
发布时间:2019-04-29

本文共 3031 字,大约阅读时间需要 10 分钟。

导读:本文主要简要列举下Google、Tencent、Alibaba以及Huawei等各大公司在WSDM 2020上关于深度推荐系统与CTR预估相关的论文。

1. Interpretable Click-Through Rate Prediction through Hierarchical Attention

Zeyu Li (University of California Los Angeles, United States); Wei Cheng (NEC Laboratories America, United States); Yang Chen (Google, United States); Haifeng Chen (NEC Laboratories America, United States); Wei Wang (University of California Los Angeles, United States).

论文:http://t.cn/A6LTtVFl

代码:https://github.com/zyli93/InterHAt

2. A Stochastic Treatment of Learning to Rank Scoring Functions

Sebastian Bruch, Shuguang Han, Michael Bendersky, Marc Najork (Google Research, United States).

论文:http://t.cn/A6LTtJYV

3. Addressing Marketing Bias in Product Recommendations

Mengting Wan, Jianmo Ni (University of California San Diego, United States); Rishabh Misra (Twitter, United States); Julian McAuley (University of California San Diego, United States).

论文:https://arxiv.org/abs/1912.01799

数据集:http://t.cn/A6LTtSYV

4. Initialization for Network Embedding: A Graph Partition Approach

Wenqing Lin, Feng He, Faqiang Zhang, Xu Cheng, Hongyun Cai (Tencent, China).

论文:https://arxiv.org/abs/1908.10697

5. Popularity Prediction on Social Platforms with Coupled Graph Neural Networks

Qi Cao, Huawei Shen, Jinhua Gao (Chinese Academy of Sciences, China); Bingzheng Wei (Tencent Inc, China); Xueqi Cheng (Chinese Academy of Sciences, China).

论文:https://arxiv.org/abs/1906.09032

6. End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding

Feng Liu (Harbin Institute of Technology, China); Huifeng Guo (Huawei, China); Xutao Li (Harbin Institute of Technology, China); Ruiming Tang (Huawei, China); Yunming Ye (Harbin Institute of Technology, China); Xiuqiang He (Huawei, China).

论文:http://t.cn/A6LTtCsS

7. Fast Item Ranking under Neural Network based Measures

Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li (Baidu Research, United States).

论文:http://t.cn/A6LTt0nT

8. Parameter Tuning in Personal Search Systems

Suming J. Chen, Xuanhui Wang, Zhen Qin, Donald Metzler (Google, United States).

论文:http://t.cn/A6LTtW8K

9. Ultra Fine-Grained Image Semantic Embedding

Da-Cheng Juan, Chun-Ta Lu, Zhen Li, Futang Peng, Aleksei Timofeev, Yi-Ting Chen, Yaxi Gao, Tom Duerig, Andrew Tomkins, Sujith Ravi (Google Research, United States).

论文:http://t.cn/A6LTtjih

10. MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph

Xin Mao (East China Normal University, China); Wenting Wang (Alibaba Group, China); Huimin Xu, Man Lan, Yuanbin Wu (East China Normal University, China).

论文:http://t.cn/A6LTtT7r

代码:https://github.com/MaoXinn/MRAEA

11. Separate and Attend in Personal Email Search

Yu Meng (University of Illinois at Urbana-Champaign, United States); Maryam Karimzadehgan, Honglei Zhuang, Donald Metzler (Google, United States).

论文:https://arxiv.org/abs/1911.09732

更多WSDM 2020 accepted paper list请点击文末左下角原文链接查看。本文中涉及到的所有论文以及更多最前沿的推荐广告方面的论文分享请移步如下的GitHub项目进行学习交流star以及fork,后续仓库会持续更新最新论文。

https://github.com/imsheridan/DeepRec

转载地址:http://ssrqf.baihongyu.com/

你可能感兴趣的文章
一文了解强化学习
查看>>
CART 分类与回归树
查看>>
seq2seq 的 keras 实现
查看>>
seq2seq 入门
查看>>
什么是 Dropout
查看>>
用 LSTM 做时间序列预测的一个小例子
查看>>
用 LSTM 来做一个分类小问题
查看>>
详解 LSTM
查看>>
按时间轴简述九大卷积神经网络
查看>>
详解循环神经网络(Recurrent Neural Network)
查看>>
为什么要用交叉验证
查看>>
用学习曲线 learning curve 来判别过拟合问题
查看>>
用验证曲线 validation curve 选择超参数
查看>>
用 Grid Search 对 SVM 进行调参
查看>>
用 Pipeline 将训练集参数重复应用到测试集
查看>>
PCA 的数学原理和可视化效果
查看>>
机器学习中常用评估指标汇总
查看>>
什么是 ROC AUC
查看>>
Bagging 简述
查看>>
详解 Stacking 的 python 实现
查看>>