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Title: GitHub - tangxyw/RecAlgorithm: 主流推荐系统Rank算法的实现

Open Graph Title: GitHub - tangxyw/RecAlgorithm: 主流推荐系统Rank算法的实现

X Title: GitHub - tangxyw/RecAlgorithm: 主流推荐系统Rank算法的实现

Description: 主流推荐系统Rank算法的实现. Contribute to tangxyw/RecAlgorithm development by creating an account on GitHub.

Open Graph Description: 主流推荐系统Rank算法的实现. Contribute to tangxyw/RecAlgorithm development by creating an account on GitHub.

X Description: 主流推荐系统Rank算法的实现. Contribute to tangxyw/RecAlgorithm development by creating an account on GitHub.

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https://patch-diff.githubusercontent.com/tangxyw/RecAlgorithm#主流推荐系统rank算法的实现
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https://patch-diff.githubusercontent.com/tangxyw/RecAlgorithm#项目简介
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https://patch-diff.githubusercontent.com/tangxyw/RecAlgorithm#单任务models列表
Field-aware Factorization Machines for CTR Predictionhttps://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf
Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Featureshttps://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf
Product-based neural networks for user response predictionhttps://arxiv.org/pdf/1611.00144.pdf
Wide & Deep Learning for Recommender Systemshttps://arxiv.org/pdf/1606.07792.pdf
DeepFM: A Factorization-Machine based Neural Network for CTR Predictionhttp://www.ijcai.org/proceedings/2017/0239.pdf
Deep & Cross Network for Ad Click Predictionshttps://arxiv.org/abs/1708.05123
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkshttp://www.ijcai.org/proceedings/2017/435
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systemshttps://arxiv.org/pdf/1803.05170.pdf
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertisinghttps://arxiv.org/pdf/1806.03514.pdf
Deep Interest Network for Click-Through Rate Predictionhttps://arxiv.org/pdf/1706.06978.pdf
Deep Interest Evolution Network for Click-Through Rate Predictionhttps://arxiv.org/pdf/1809.03672.pdf
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Predictionhttps://arxiv.org/pdf/1905.09433.pdf
Behavior sequence transformer for e-commerce recommendation in Alibabahttps://arxiv.org/pdf/1905.06874.pdf
https://patch-diff.githubusercontent.com/tangxyw/RecAlgorithm#多任务models列表
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Ratehttps://arxiv.org/abs/1804.07931
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Expertshttps://dl.acm.org/doi/abs/10.1145/3219819.3220007
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendationshttps://dl.acm.org/doi/10.1145/3383313.3412236
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