Search Ranking And Personalization at Airbnb
Search ranking is a fundamental problem 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 and the parties affected by ranking, each search ranking problem is somewhat specific. Correspondingly, search ranking at Airbnb is 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 talk, I will discuss challenges we have encountered and Machine Learning solutions we have developed for listing ranking at Airbnb. Specifically, the listing ranking problem boils down to prioritizing listings that are appealing to the guest but at the same time demoting listings tha t would likely reject the guest, which is not easily solvable using basic matrix completion or a straightforward linear model. I will shed the light on how we jointly optimize the two objectives by leveraging listing quality, location relevance, reviews, host response time as well as guest and host preferences and past booking history. Finally, we will talk about our recent work on using neural network models to train listing, query and user embeddings for purposes of enhancing search relevance and personalization, two core concepts in any modern search.
Mihajlo Grbovic, Ph.D. is a Senior Machine Learning Scientist on the Search Ranking Team at Airbnb. Prior to that, he was a Senior Research Manager at Yahoo Labs working on Advertising Sciences. He has more than 10 years of technical experience in applied Machine Learning, acting as a Science Lead in a portfolio of advertising technology projects on Monetization of Tumblr, Yahoo Email and Yahoo Search. Some of his biggest accomplishments include building a large scale Interest and Gender Targeting Pipeline for Tumblr, training Email Classifiers used in Yahoo Mail Smart Views that millions of people interact with every day, and introducing the next generation query-ad matching algorithm to Yahoo Sponsored Search. Dr. Grbovic published more than 40 peer-reviewed publications at top Machine Learning and Web Science Conferences and co-authored more than 10 pending patents. His work was featured in Wall Street Journal, Scientific American, MIT Technology Review, Popular Science and Market Watch.