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
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
- http://users.cecs.anu.edu.au/~akmenon/papers/autorec/autorec-paper.pdf
- https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf
- https://arxiv.org/pdf/1708.05031.pdf
- https://arxiv.org/pdf/1611.00144.pdf
- https://arxiv.org/pdf/1606.07792.pdf
- https://dl.acm.org/doi/pdf/10.1145/3124749.3124754
- https://arxiv.org/pdf/1601.02376.pdf
- https://www.ijcai.org/Proceedings/2017/0239.pdf
- https://arxiv.org/pdf/1708.05027v1.pdf
- https://arxiv.org/pdf/1708.04617.pdf
- https://arxiv.org/pdf/1706.06978.pdf
- https://arxiv.org/pdf/1809.03672.pdf
- https://arxiv.org/pdf/1906.00091.pdf
- https://astro.temple.edu/~tua95067/kdd2018.pdf
- https://dl.acm.org/doi/pdf/10.1145/2959100.2959190
- https://arxiv.org/pdf/1905.09248.pdf
- https://arxiv.org/pdf/1804.07931.pdf
- https://arxiv.org/pdf/1711.06505.pdf