推荐系统论文集

传统推荐算法

1. Collaborative Filtering (CF) - 协同过滤算法

• Using collaborative filtering to weave an information tapestry 【1992年】
  https://dl.acm.org/doi/10.1145/138859.138867
• Amazon.com recommendations: item-to-item collaborative filtering 【2003年】
  https://ieeexplore.ieee.org/abstract/document/1167344

2. Matrix factorization (MF) - 矩阵分解算法

• Matrix Factorization Techniques for Recommender Systems 【2009年】
  https://ieeexplore.ieee.org/abstract/document/5197422

3. Logistic regression (LR) - 逻辑回归算法

4. Factorization machines (FM) - 因子分解机

• Factorization Machines 【2010年】
  https://ieeexplore.ieee.org/abstract/document/5694074
• Field-aware Factorization Machines for CTR Prediction 【FFM算法，2016年】
  https://ieeexplore.ieee.org/abstract/document/5694074

5. GBDT+LR （Facebook）

• Practical Lessons from Predicting Clicks on Ads at Facebook 【2014年】
  https://dl.acm.org/doi/abs/10.1145/2648584.2648589

6. LS-PLM （阿里巴巴）

• Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction 【2017年】
  https://arxiv.org/abs/1704.05194

深度学习推荐算法

1. AutoRec - 单隐层神经网络推荐模型

• AutoRec: Autoencoders Meet Collaborative Filtering
  http://users.cecs.anu.edu.au/~akmenon/papers/autorec/autorec-paper.pdf

2. Deep Crossing - 经典的深度学习架构

• Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features
  https://dl.acm.org/doi/abs/10.1145/2939672.2939704

3. Neural collaborative filtering （NCF）- CF与深度学习的结合

• Neural Collaborative Filtering
  https://dl.acm.org/doi/abs/10.1145/3038912.3052569

4. PNN - 加强特征交叉能力

• Product-Based Neural Networks for User Response Prediction
  https://ieeexplore.ieee.org/abstract/document/7837964

5. Wide & Deep - 记忆能力与泛化能力的综合

• Wide & Deep Learning for Recommender Systems 【Wide & Deep 模型】
  https://dl.acm.org/doi/abs/10.1145/2988450.2988454
• Deep & Cross Network for Ad Click Predictions 【Deep & Cross模型】
  https://arxiv.org/abs/1708.05123

6. FM与深度学习的结合

• Deep Learning over Multi-field Categorical Data【FNN模型】
  https://link.springer.com/chapter/10.1007/978-3-319-30671-1_4
• DeepFM: A Factorization-Machine based Neural Network for CTR Prediction 【DeepFM模型】
  https://www.ijcai.org/Proceedings/2017/0239.pdf
• Neural Factorization Machines for Sparse Predictive Analytics【NFM模型】
  https://arxiv.org/abs/1708.05027

7. 注意力机制在推荐模型中的应用

• Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks 【AFM模型】
  https://arxiv.org/abs/1708.04617
• Deep Interest Network for Click-Through Rate Prediction 【DIN模型】
  https://arxiv.org/abs/1706.06978

8. 序列模型与推荐系统的结合

• Deep Interest Evolution Network for Click-Through Rate Prediction 【DIEN模型】
  https://arxiv.org/abs/1809.03672

9. 强化学习与推荐系统的结合

• DRN: A Deep Reinforcement Learning Framework for News 【DRN模型】
  Recommendation
  https://www.personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf

• Deep Neural Networks for YouTube Recommendations
  https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf