动态更新工作中实现或者阅读过的推荐系统相关论文、学习资料和业界分享，作为自己工作的总结，也希望能为推荐系统相关行业的同学带来便利。 所有资料均来自于互联网，如有侵权，请联系王喆。同时欢迎对推荐系统感兴趣的同学与我讨论相关问题，我的联系方式如下：
- Email: wzhe06@gmail.com
- LinkedIn: 王喆的LinkedIn
- 知乎私信: 王喆的知乎
其他相关资源
- 张伟楠的RTB Papers列表
- 基于Spark MLlib的CTR prediction模型(LR, Random forest, GBDT, NN, PNN)
- 推荐系统相关论文和资源列表
- Honglei Zhang的推荐系统论文列表
推荐系统模型的经典文章。
- Item2Vec - Neural Item Embedding for Collaborative Filtering.pdf
- Neural Collaborative Filtering.pdf
- 微博推荐策略平台Eros.pdf
- DeepFM- A Factorization-Machine based Neural Network for CTR Prediction.pdf
- Deep Neural Networks for YouTube Recommendations.pdf
- Matrix Factorization Techniques for Recommender Systems.pdf
- Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models.pdf
- Wide & Deep Learning for Recommender Systems.pdf
Google的Wide&Deep经典深度学习推荐模型 - 基于BPR-MF算法的推荐系统设计.docx
- Quora - Building a Machine Learning Platform at Quora.pdf
- Pinterest - Personalized content blending In the Pinterest home feed.pdf
- [Word2Vec] Efficient Estimation of Word Representations in Vector Space.pdf
- [CNN] ImageNet Classification with Deep Convolutional Neural Networks.pdf
- [Word2Vec]Distributed Representations of Words and Phrases and their Compositionality.pdf
- [RNN] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation.pdf
- Recent Advances in Recommender Systems- Matrices, Bandits, and Blenders.pdf
- The Netflix Recommender System- Algorithms, Business Value, and Innovation.pdf
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Exploration in Interactive Personalized Music Recommendation- A Reinforcement Learning Approach.pdf
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Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques.pdf
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Offline Evaluation and Optimization for Interactive Systems.pdf
探索和利用问题，推荐系统中非常重要的一个领域。
- An Empirical Evaluation of Thompson Sampling.pdf
- Dynamic Online Pricing with Incomplete Information Using Multi-Armed Bandit Experiments.pdf
- Finite-time Analysis of the Multiarmed Bandit Problem.pdf
- A Fast and Simple Algorithm for Contextual Bandits.pdf
- Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments.pdf
- Mastering the game of Go with deep neural networks and tree search.pdf
- Exploring compact reinforcement-learning representations with linear regression.pdf
- Thompson Sampling for Contextual Bandits with Linear Payoffs.pdf
- Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms.pdf
- Incentivizting Exploration in Reinforcement Learning with Deep Predictive Models.pdf
- Exploration and Exploitation Problem by Wang Zhe.pdf
- Bandit Algorithms Continued- UCB1.pdf
- A Contextual-Bandit Approach to Personalized News Article Recommendation(LinUCB).pdf
- Exploitation and Exploration in a Performance based Contextual Advertising System.pdf
- Bandit based Monte-Carlo Planning.pdf
- Random Forest for the Contextual Bandit Problem.pdf
- Unifying Count-Based Exploration and Intrinsic Motivation.pdf
- Explore, Exploit, and Explain- Personalizing Explainable Recommendations with Bandits.pdf
- Analysis of Thompson Sampling for the Multi-armed Bandit Problem.pdf
- Thompson Sampling PPT.pdf
- Hierarchical Deep Reinforcement Learning- Integrating Temporal Abstraction and Intrinsic Motivation.pdf
- Exploration and Exploitation Problem by Wang Zhe.pptx
- Exploration exploitation in Go UCT for Monte-Carlo Go.pdf
- 对抗搜索、多臂老虎机问题、UCB算法.ppt
- Using Confidence Bounds for Exploitation-Exploration Trade-offs.pdf