Applying Deep Learning To Airbnb Search

Random Forests. In Machine Learning . 5–32

Leo Breiman Statistics and Leo Breiman. 2001 · 2001

Earlier work this paper cites.

From RankNet to LambdaRank to LambdaMART: An Overview

Chris J.C. Burges. 2010 · 2010

Earlier work this paper cites.

Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research) , Yee Whye Teh and Mike Titterington (Eds.), Vol. 9. PMLR, Chia Laguna Resort, Sardinia, Italy, 249–256

Xavier Glorot and Yoshua Bengio. 2010 · 2010

Earlier work this paper cites.

Factorization Machines with libFM

Steffen Rendle. 2012 · 2012

Earlier work this paper cites.

Dropout Training As Adaptive Regularization. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 1 (NIPS’13) . Curran Associates Inc., USA, 351–359

Stefan Wager, Sida Wang, and Percy Liang. 2013 · 2013

Earlier work this paper cites.

Adam: A Method for Stochastic Optimization

Original

Diederik P. Kingma and Jimmy Ba. 2014 · 2014

Earlier work this paper cites.

Beyond Clicks: Dwell Time for Personalization. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14) . ACM, New York, NY, USA, 113–120

Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, and Suju Rajan. 2014 · 2014

Earlier work this paper cites.

A Primer on Neural Network Models for Natural Language Processing

Original

Yoav Goldberg. 2015 · 2015

Earlier work this paper cites.

Hidden Technical Debt in Machine Learning Systems. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (NIPS’15) . 2503–2511

D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015 · 2015

Earlier work this paper cites.

Dropout as data augmentation. In arXiv e-prints

Xavier Bouthillier, Kishore Konda, Pascal Vincent, and Roland Memisevic. 2016 · 2016

Cited alongside, same era.

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). In Proceedings of International Conference on Learning Representations 2016 (ICLR’16)

Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2016 · 2016

Cited alongside, same era.

Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems . New York, NY, USA

Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016

Cited alongside, same era.

Amazon Search: The Joy of Ranking Products. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’16) . 459–460

Daria Sorokina and Erick Cantu-Paz. 2016 · 2016

Cited alongside, same era.

On Application of Learning to Rank for E-Commerce Search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’17) . ACM, 475–484

Deep & Cross Network for Ad Click Predictions. In Proceedings of the ADKDD’17 (ADKDD’17) . ACM, New York, NY, USA, Article 12, 7 pages

Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017

Later among the works it cites.

Understanding deep learning requires rethinking generalization

Original

Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017 · 2017

Later among the works it cites.

Real-time Personalization using Embeddings for Search Ranking at Airbnb. In Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Mihajlo Grbovic and Haibin Cheng. 2018 · 2018

Closest in time.

CS231n Convolutional Neural Networks for Visual Recognition

Andrej Karpathy. 2018 · 2018

Closest in time.

Partial Dependence Plots

Scikit learn Documentation. 2018 · 2018

Closest in time.