Presentation: Applying Deep Learning To Airbnb Search
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Watch video with transcriptAbstract
Searching for homes is the primary mechanism guests use to find the place they want to book at Airbnb. The goal of search ranking is to find guests the best possible options while rewarding the most deserving hosts. Ranking at Airbnb is a quest to understand the needs of the guests and the quality of the hosts to strike the best match possible. Applying machine learning to this challenge is one of the biggest success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This talk discusses the work done in applying neural networks in an attempt to break out of that plateau. The talk focuses on the elements we found useful in applying neural networks to a real life product. To other teams embarking on similar journeys, we hope this account of our struggles and triumphs will provide some useful pointers. Bon voyage!
2019 Tracks
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Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
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Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.
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Predictive Data Pipelines & Architectures
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix
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Sequential Data: Natural Language, Time Series, and Sound
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
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ML in Action
Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud
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Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
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Handling Sequential Data Like an Expert / ML Applied to Operations
Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.
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AI Meets the Physical World
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Hands-on Codelabs & Speakers Office Hours
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Papers in Production: Modern CS in the Real World
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Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.