推荐系统、计算广告、NLP、生物信号等领域文章

更新实现或阅读过的相关论文、学习资料，欢迎感兴趣的同学讨论相关问题，联系方式如下：

Email: [email protected]

相关资源

• 王喆的计算广告论文列表
• 王喆的推荐系统论文列表
• 王喆的基于Spark MLlib的CTR预估模型
• 张伟楠的RTB论文列表
• Zhang Honglei的推荐系统论文列表
• DeepCTR作者的相关论文列表

目录

1、Recommendation system and CTR Prediction

推荐系统、计算广告、CTR预估相关论文和模型

• 【PMF】Probabilistic Matrix Factorization (2007, NIPS, 多伦多大学)
• 【FM】Factorization Machines (2011, ICDM, 日本大阪大学)
• 【FFM】Field-aware Factorization Machines for CTR Prediction (2016, RecSys, Criteo)
• 【Wide&Deep】Wide&Deep Learning for Recommender Systems (2016, RecSys, 谷歌)
• 【Deep FM】DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017, IJCAI, 华为+哈工大)
• 【MLR】Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (2017, 阿里巴巴)
• 【DIN】Deep Interest Network for Click-Through Rate Prediction (2018, SIGKDD, 阿里巴巴)
• 【YouTube】Deep Neural Networks for YouTube Recommendations (2016, YouTube)

1-1 推荐系统最新研究进展（2018-09更新）

参考链接

• A review on deep learning for recommender systems: challenges and remedies

2、sEMG for Deep Learning

基于肌电信号、脑电信号的动作意图解码，常用方法为：人工特征提取+机器学习算法

深度学习能够实现端到端的动作识别，常用模型：一维卷积、二维卷积、多流卷积

• 【NinaPro DataSet】Electromyography data for non-invasive naturally-controlled Robotic hand prostheses (2014, Nature, 瑞士西部应用科学大学)
• 【NinaPro CNN】Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, Frontiers in Neurorobotics, 瑞士西部应用科学大学)
• 【EEG CNN】Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG (2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 英国萨里大学)
• 【Multi-stream CNN】Hand Gesture Recognition Based On Deep Learning Method (2018, IEEE International Conference on Data Science in Cyberspace, 哈尔滨工业大学)

准备看的

• Recurrent Neural Network Based Early Prediction of Future Hand Movements
• Recurrent Neural Networks with Weighting Loss for Early Prediction of Hand Movements
• Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron
• A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface
• Gesture recognition by instantaneous surface EMG images
• Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
• Semi-Supervised Learning for Surface EMG-based Gesture Recognition