The 2018 KDD conference
is right around the corner — and we are looking forward to seeing you
there. (That’s Knowledge Discovery and Data Mining, if you’re not
familiar with it!) We are excited to have three Airbnb papers accepted
for oral presentation at the Applied Data Science Track and one tech
talk presentation. Our team will share our approach to solving
interesting and challenging problems at Airbnb, including personalized
real-time search ranking, dynamic pricing, paid growth, and online
experimentation.
These
are just a few examples of advanced technology of machine learning,
artificial intelligence, and online experimentation we leverage at
Airbnb to help create a world where everyone can belong everywhere.
We
can’t wait to both share our work and learn from all of you in London
next week. Don’t hesitate to come and say hi to our team — and remember,
we’re hiring!
Our Papers
Real-time Personalization using Embeddings for Search Ranking at Airbnb by Mihajlo Grbovic (Airbnb); Haibin Cheng (Airbnb)
Search
Ranking and Recommendations are fundamental problems of crucial
interest to major Internet companies, including web search engines,
content publishing websites and marketplaces. However, despite sharing
some common characteristics a one-size-fits-all solution does not exist
in this space. Given a large difference in content that needs to be
ranked, personalized and recommended, each marketplace has a somewhat
unique challenge. Correspondingly, at Airbnb, a short-term rental
marketplace, search and recommendation problems are quite unique, being a
two-sided marketplace in which one needs to optimize for host and guest
preferences, in a world where a user rarely consumes the same item
twice and one listing can accept only one guest for a certain set of
dates.
In
this paper we describe Listing and User Embedding techniques we
developed and deployed for purposes of Real-time Personalization in
Search Ranking and Similar Listing Recommendations, two channels that
drive 99% of conversions. The embedding models were specifically
tailored for Airbnb marketplace, and are able to capture guest’s
short-term and long-term interests, delivering effective home listing
recommendations. We conducted rigorous offline testing of the embedding
models, followed by successful online tests before fully deploying them
into production.
The summary of this paper was published in Airbnb’s Medium Blog.
Customized Regression Model for Airbnb Dynamic Pricing
by Peng Ye (Airbnb); Julian Qian (Ant financial); Jieying Chen
(Airbnb); Chen-Hung Wu (Airbnb); Yitong Zhou (Airbnb); Spencer De Mars
(Impira); Frank Yang (Airbnb); Li Zhang (Airbnb)
This
paper describes the pricing strategy model deployed at Airbnb, an
online marketplace for sharing home and experience. The goal of price
optimization is to help hosts who share their homes on Airbnb set the
optimal price for their listings. In contrast to conventional pricing
problems, where pricing strategies are applied to a large quantity of
identical products, there are no “identical” products on Airbnb, because
each listing on our platform offers unique values and experiences to
our guests. The unique nature of Airbnb listings makes it very difficult
to estimate an accurate demand curve that’s required to apply
conventional revenue maximization pricing strategies.
Our
pricing system consists of three components. First, a binary
classification model predicts the booking probability of each
listing-night. Second, a regression model predicts the optimal price for
each listing-night, in which a customized loss function is used to
guide the learning. Finally, we apply additional personalization logic
on top of the output from the second model to generate the final price
suggestions. In this paper, we focus on describing the regression model
in the second stage of our pricing system. We also describe a novel set
of metrics for offline evaluation. The proposed pricing strategy has
been deployed in production to power the Price Tips and Smart Pricing
tool on Airbnb. Online A/B testing results demonstrate the effectiveness
of the proposed strategy model.
Winner’s Curse: Bias Estimation for Total Effects of Features in Online Controlled Experiments by Minyong Lee (Airbnb); Milan Shen (Airbnb)
Online
controlled experiments, or A/B testing, has been a standard framework
adopted by most online product companies to measure the effect of any
new change. Companies use various statistical methods including
hypothesis testing and statistical inference to quantify the business
impact of the changes and make product decisions. Nowadays,
experimentation platforms can run as many as hundreds or even more
experiments concurrently. When a group of experiments is conducted,
usually the ones with significant successful results are chosen to be
launched into the product. We are interested in learning the aggregated
impact of the launched features. In this paper, we investigate a
statistical selection bias in this process and propose a correction
method of getting an unbiased estimator. Moreover, we give an
implementation example at Airbnb’s ERF platform (Experiment Reporting
Framework) and discuss the best practices to account for this bias.
Invited Tech Talk
Ganesh Venkataraman will be delivering an invited talk at Ad KDD workshop.
The growth team at Airbnb is responsible for helping travelers find
Airbnb, in part by participating in ad auctions on major search
platforms such as Google and Bing. In this talk, we will describe how
advertising efficiently on these platforms requires solving several
information retrieval and machine learning problems, including query
understanding, click value estimation, attribution and realtime pacing
of our expenditure and bidding optimization.
The Airbnb Booth at KDD
Please
feel free to stop by our booth (#11) to meet Airbnb engineers and data
scientists who work on a variety of machine learning problems.
Airbnb Events at KDD
We
will be hosting an invite only event on Tuesday night. Join us Tuesday
at The Gun to meet more members of the team and hear music from Hidden
Jazz experience hosts from Airbnb’s community in London. We’ll have
food, drinks, music and great conversation!
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