Recommending what video to watch next: a multitask ranking system
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Li Wei | Zhe Zhao | Ed H. Chi | Ed Huai-hsin Chi | Xinyang Yi | Shawn Andrews | Lichan Hong | Jilin Chen | Maheswaran Sathiamoorthy | Aniruddh Nath | Aditee Kumthekar | Lichan Hong | Xinyang Yi | Jilin Chen | Zhe Zhao | A. Nath | M. Sathiamoorthy | Li Wei | A. Kumthekar | Shawn Andrews | Aniruddh Nath
[1] Xin Zhang,et al. TFX: A TensorFlow-Based Production-Scale Machine Learning Platform , 2017, KDD.
[2] Ed H. Chi,et al. Towards Neural Mixture Recommender for Long Range Dependent User Sequences , 2019, WWW.
[3] Yue Yin,et al. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data , 2018, SIGIR.
[4] Zhe Zhao,et al. Improving User Topic Interest Profiles by Behavior Factorization , 2015, WWW.
[5] Paul Covington,et al. Deep Neural Networks for YouTube Recommendations , 2016, RecSys.
[6] Boi Faltings,et al. Offline and online evaluation of news recommender systems at swissinfo.ch , 2014, RecSys '14.
[7] Thorsten Joachims,et al. Unbiased Learning-to-Rank with Biased Feedback , 2016, WSDM.
[8] Jimmy J. Lin,et al. WTF: the who to follow service at Twitter , 2013, WWW.
[9] Gang Fu,et al. Deep & Cross Network for Ad Click Predictions , 2017, ADKDD@KDD.
[10] Filip Radlinski,et al. Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search , 2007, TOIS.
[11] Mohamed A. Sharaf,et al. MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).
[12] George Karypis,et al. Multi-task learning for recommender systems , 2010, ACML 2010.
[13] Ji-Rong Wen,et al. A Neural Labeled Network Embedding Approach to Product Adopter Prediction , 2018, AIRS.
[14] Philip S. Yu,et al. Learning Multiple Tasks with Multilinear Relationship Networks , 2015, NIPS.
[15] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[16] Zhe Zhao,et al. Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations , 2017, ArXiv.
[17] Barry Smyth,et al. Why I like it: multi-task learning for recommendation and explanation , 2018, RecSys.
[18] Ed H. Chi,et al. SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning , 2019, AAAI.
[19] Tie-Yan Liu,et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.
[20] Trevor Darrell,et al. Visual Discovery at Pinterest , 2017, WWW.
[21] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[23] Zhe Zhao,et al. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts , 2018, KDD.
[24] Marc Najork,et al. Learning to Rank with Selection Bias in Personal Search , 2016, SIGIR.
[25] Geoffrey E. Hinton,et al. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer , 2017, ICLR.
[26] Olivier Chapelle,et al. A dynamic bayesian network click model for web search ranking , 2009, WWW '09.
[27] Stephanie Rogers,et al. Related Pins at Pinterest: The Evolution of a Real-World Recommender System , 2017, WWW.
[28] Maoguo Gong,et al. Multi-objective optimization for long tail recommendation , 2016, Knowl. Based Syst..
[29] George Karypis,et al. Multi-task Learning for Recommender System , 2010, ACML.
[30] Thorsten Joachims,et al. Estimating Position Bias without Intrusive Interventions , 2018, WSDM.
[31] Lina Yao,et al. Deep Learning Based Recommender System , 2017, ACM Comput. Surv..
[32] Thorsten Joachims,et al. Batch learning from logged bandit feedback through counterfactual risk minimization , 2015, J. Mach. Learn. Res..
[33] Jianmin Wang,et al. Learning Multiple Tasks with Deep Relationship Networks , 2015, ArXiv.
[34] Jure Leskovec,et al. Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time , 2017, WWW.
[35] Deepak Agarwal,et al. Localized factor models for multi-context recommendation , 2011, KDD.
[36] Yu He,et al. The YouTube video recommendation system , 2010, RecSys '10.
[37] Karthik Ramani,et al. Deconvolving Feedback Loops in Recommender Systems , 2016, NIPS.
[38] Antonino Freno,et al. Practical Lessons from Developing a Large-Scale Recommender System at Zalando , 2017, RecSys.
[39] Zhoujun Li,et al. Integrating rich information for video recommendation with multi-task rank aggregation , 2011, ACM Multimedia.
[40] Xiaodong He,et al. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems , 2015, WWW.
[41] John R. Anderson,et al. Efficient Training on Very Large Corpora via Gramian Estimation , 2018, ICLR.
[42] Abien Fred Agarap. Deep Learning using Rectified Linear Units (ReLU) , 2018, ArXiv.
[43] Ke Wang,et al. Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System , 2018, KDD.
[44] Gregory N. Hullender,et al. Learning to rank using gradient descent , 2005, ICML.
[45] Heng-Tze Cheng,et al. Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.
[46] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[47] Joaquin Quiñonero Candela,et al. Practical Lessons from Predicting Clicks on Ads at Facebook , 2014, ADKDD'14.