MALAY HALDAR / April 2019 / QCon Applying Deep Learning To Airbnb Search
Unique Challenges High Value Transactions Sparse Listing Data Sparse User Data Users are picky when spending Each listing can be booked at Travel is not a daily use case. hundreds of dollars. most 365 times a year.
How Does Airbnb Search Work?
Evolution Of Search Ranking At Airbnb DNN GBDT Scoring Functions 2014 2015 2016 2017 2018 2019
First Neural Net : April 2017 L2 loss 31 Relus { Continuous features with heavy feature engineering }
● Keep the first launch as simple as possible Key Learnings ● Manage expectations, aim for neutral
Second Neural Net: Lambdarank NN June 2017 sigmoid_cross_entropy_with_logits X lambdarank_weights - { Booked listing features } { Unbooked listing features }
● Pairwise preference Key Learnings ● Tensorflow
Third Neural Net: Hybrid Mar 2018
DNN : June 2018 83 Relus 127 Relus { Raw query and listing stats }
● Transitioning to deep learning is about scaling the system. Key Learnings ● Complexity is easy to build, simplicity is hard.
Gains
Problem Formulation
Pointwise
Pairwise
Do Not Cook Your Features Feature Engineering
Continuous Features
Embedding -1.203 -1.862 Categorical 1.701 Feature -2.003 ( 0x3fc90 ) 0.192 : Categorical Features
● I/O System ● I/O Engineering ● I/O
https://christophm.github.io/interpretable-ml-book/pdp.html ...This problem is easily solved by showing a rug (indicators for data points on the x-axis) or a histogram. Feature The assumption of independence is the biggest Importance issue with PD plots. It is assumed that the feature(s) for which the partial dependence is computed are not correlated with other features. For example, suppose you want to predict ...
TopBot Analysis
Hyperparameter Optional, not a necessity. Tuning
Many more interesting Failed models! topics...
Applying Deep Learning To Airbnb Search Malay Haldar, Mustafa Abdool, Prashant Ramanathan, Tao Xu, Shulin Yang, Huizhong Duan, Qing Zhang, Nick Barrow-Williams, Bradley C. Turnbull, Brendan M. Collins, Thomas Legrand https://arxiv.org/abs/1810.09591
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