深度学习推荐系统论文汇总

AutoRec

AutoRec: AutoRec: Autoencoders Meet Collaborative Filtering

Deep Crossing

Deep Crossing: Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features

NeuralCF

NeuralCF: Neural Collaborative Filtering

PNN

PNN: Product-based Neural Networks for User Response Prediction

Wide&Deep

Wide&Deep: Wide & Deep Learning for Recommender Systems

Deep&Cross

Deep & Cross: Deep & Cross Network for Ad Click Predictions

FNN

FNN: Deep Learning over Multi-field Categorical Data

DeepFM

DeepFM: DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

NFM

NFM: Neural Factorization Machines for Sparse Predictive Analytics

AFM

AFM: Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

DIN

DIN: Deep Interest Network for Click-Through Rate Prediction

DIEN

DIEN: Deep Interest Evolution Network for Click-Through Rate Prediction

DLRM

DLRM: Deep Learning Recommendation Model for Personalization and Recommendation Systems

RPES

RPES: Real-time Personalization using Embeddings for Search Ranking at Airbnb

YoutubeRec

YoutubeRec: Deep Neural Networks for YouTube Recommendations

MIMN

MIMN: Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

ESMM

ESMM: Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

DICM

DICM: Image Matters: Visually modeling user behaviors using Advanced Model Server

参考文献

  1. http://users.cecs.anu.edu.au/~akmenon/papers/autorec/autorec-paper.pdf
  2. https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf
  3. https://arxiv.org/pdf/1708.05031.pdf
  4. https://arxiv.org/pdf/1611.00144.pdf
  5. https://arxiv.org/pdf/1606.07792.pdf
  6. https://dl.acm.org/doi/pdf/10.1145/3124749.3124754
  7. https://arxiv.org/pdf/1601.02376.pdf
  8. https://www.ijcai.org/Proceedings/2017/0239.pdf
  9. https://arxiv.org/pdf/1708.05027v1.pdf
  10. https://arxiv.org/pdf/1708.04617.pdf
  11. https://arxiv.org/pdf/1706.06978.pdf
  12. https://arxiv.org/pdf/1809.03672.pdf
  13. https://arxiv.org/pdf/1906.00091.pdf
  14. https://astro.temple.edu/~tua95067/kdd2018.pdf
  15. https://dl.acm.org/doi/pdf/10.1145/2959100.2959190
  16. https://arxiv.org/pdf/1905.09248.pdf
  17. https://arxiv.org/pdf/1804.07931.pdf
  18. https://arxiv.org/pdf/1711.06505.pdf
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