Google论文"Wide & Deep Learning for Recommender Systems"全套工程文件+数据集+调试过程

google于2016年发表的论文"Wide & Deep Learning for Recommender Systems"里面的代码； 全套工程文件+数据集+调试过程； 经过tf版本升级之后，通过调试，没有问题； 其他小问题可以参考我写的“调试过程”

Wide &Deep learning for Recommender Systems

wide&deep 广度深度模型 协同过滤

2017-Deep Matrix Factorization Models for Recommender Systems.pdf

2017-Deep Matrix Factorization Models for Recommender Systems

A review on deep learning for recommender systems_challenges and remedies.pdf

深度学习应用在推荐系统的综述，很nice。深度学习应用在推荐系统的综述，很nice。深度学习应用在推荐系统的综述，很nice。

Practical Recommender Systems.pdf

Practical Recommender Systems.pdf推荐系统推荐系统的书

2017-Collaborative Autoencoder for Recommender Systems.pdf

2017-Collaborative Autoencoder for Recommender Systems

Recommender Systems.pdf

A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems

2018-Micro behaviors-A new perspective in e-commerce recommender systems.pdf

2018-Micro behaviors-A new perspective in e-commerce recommender systems

2018-Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf

2018-Graph Convolutional Neural Networks for Web-Scale Recommender Systems

recommender systems.pdf

最权威的推荐系统综述，看懂了就可以对推荐系统有很深刻的了解，十分有用

Combining Explicit and Implicit Feature Interactions for Recommender Systems.pdf

2018-xDeepFM- Combining Explicit and Implicit Feature Interactions for Recommender Systems

深度学习必读论文集

深度学习领域的经典论文，主要由(hiton, lecun, bengio)三巨头完成，包括： 奠基： DeepLearning_By_Three_Giant.pdf Learning Representation By Back Propagation.pdf Dropout- A Simple Way to Prevent Neural Networks from Overfitting.pdf CNN: ImageNet Classification with Deep Convolutional Neural Networks.pdf Gradient-based learning applied to document recognition.pdf 应用相关论文： Distributed Representations of Words and Phrases and their Compositionality.pdf DeepWalk Online Learning of Social Representations.pdf node2vec Scalable Feature Learning for Networks.pdf Wide & Deep Learning for Recommender Systems.pdf

Wide & Deep Learning for Recommender Systems

谷歌的Wide&Deep;模型论文。大规模离散稀疏特征的处理。

Deep Learning based Recommender System: A Survey and New Perspectives

Deep Learning based Recommender System: A Survey and New Perspectives

2016-Collaborative Denoising Auto-Encoders for Top-N Recommender Systems.pdf

Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel method, called Collaborative Denoising Auto-Encoder (CDAE), for top-N recommendation that utilizes the idea of Denoising Auto-Encoders. We demonstrate that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components.

2018-Latent Cross-Making Use of Context in Recurrent Recommender Systems.pdf

2018-Latent Cross-Making Use of Context in Recurrent Recommender Systems