A little machine learning can have a big impact.
Dan Hill, product lead at Airbnb, wrote the company’s pricing algorithm after the British-based rival startup he cofounded, Crashpadder, was acquired by Airbnb, the short-term rental giant, a few years ago.
Hill has published a plain-English article about the factors involved in Airbnb’s pricing algorithm in IEEE Spectrum, a journal for the Institute of Electrical and Electronics Engineers.
In more good news, Hill’s team has released Aerosolve, the open-source machine-learning tool on which Airbnb’s pricing algorithm relies, on the Github code-sharing platform.
The Aerosolve machine-learning package enables people to upload data to improve a set of algorithms in a way that can continuously inform the model.
Aerosolve isn’t just for travel industry applications. Airbnb Engineering suggests other uses, such as predicting household income based on demographic, map, and other data.
Yet travel startups looking for a free tool designed by data scientists to help them achieve scale and leverage may like Aerosolve as a free and powerful machine-learning package.
Airbnb used machine learning to help refine the “weights” it gave to various factors in its pricing algorithm. Hill writes:
“Here’s where the learning comes in. With knowledge about the success of its tips, our system began adjusting the weights it gives to the different characteristics about a listing—the “signals” it is getting about a particular property.
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