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Title: GitHub - ARASC/papers: papers we read about Recommendation Advertising & Search

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Description: papers we read about Recommendation Advertising & Search - ARASC/papers

Open Graph Description: papers we read about Recommendation Advertising & Search - ARASC/papers

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PAL: a position-bias aware learning framework for CTR prediction in live recommender systems https://dl.acm.org/doi/pdf/10.1145/3298689.3347033
Modeling and Simultaneously Removing Bias via Adversarial Neural Networkshttps://arxiv.org/pdf/1804.06909.pdf
A Study of Position Bias in Digital Library Recommender Systemshttps://arxiv.org/abs/1802.06565
Unbiased Learning-to-Rank with Biased Feedbackhttps://arxiv.org/pdf/1608.04468.pdf
Learning to Rank with Selection Bias in Personal Searchhttps://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45286.pdf
https://patch-diff.githubusercontent.com/ARASC/papers#multitask
A Pareto-Eficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendationhttps://dl.acm.org/doi/pdf/10.1145/3298689.3346998
Recommending What Video to Watch Next: A Multitask Ranking System https://dl.acm.org/doi/pdf/10.1145/3298689.3346997
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learninghttps://www.aaai.org/ojs/index.php/AAAI/article/view/3788/3666
Conversion Rate Prediction via Post-Click Behaviour Modelinghttps://arxiv.org/pdf/1910.07099
Deep Bayesian Multi-Target Learning for Recommender Systemshttps://arxiv.org/pdf/1902.09154.pdf
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Expertshttps://dl.acm.org/doi/pdf/10.1145/3219819.3220007
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Ratehttps://arxiv.org/pdf/1804.07931.pdf
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