{"id":32734,"date":"2018-11-13T15:22:29","date_gmt":"2018-11-13T20:22:29","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-at-airbnb\/"},"modified":"2018-11-13T15:22:29","modified_gmt":"2018-11-13T20:22:29","slug":"machine-learning-at-airbnb","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-at-airbnb\/","title":{"rendered":"Machine Learning at Airbnb"},"content":{"rendered":"

Airbnb is a two-sided rental marketplace, where market dynamics play a key role in matching guests with hosts1<\/sup>. For Airbnb, supply and demand vary drastically across different geographies, different customer preferences and different check-in dates.2<\/sup> Hence, optimizing matches between hosts and guests is critical to the success. Airbnb offers variety of accommodations; however the corporate is at advantage only when guests can easily find a host that meets their criteria.2<\/sup> And as, Airbnb adds to its \u201c3 million current listings, ensuring both guests and hosts are satisfied will become more crucial. If users can find the exact accommodation they are looking for, especially if it is at a\u00a0cheaper price, they are unlikely to revert to using hotels.\u201d2<\/sup><\/p>\n

To optimize this search process for both hosts and guests, it is important for Airbnb to understand and forecast these spatial and temporal trends in order to find better matches and improve experience1<\/sup>. Currently, Airbnb highly leverages on machine learning to crunch data and generate models. \u201cThe measurable impact that machine learning has had on Airbnb\u2019s unique technological challenge \u2014 creating great matches between guests and hosts \u2014 has been \u201cprofound,\u201d says the company\u2019s VP of engineering Mike Curtis\u201d.4<\/sup> Additionally, \u201cAirbnb\u2019s machine learning infrastructure have lowered the cost significantly to deploy new machine learning models to production\u201d.3<\/sup><\/p>\n

How are they addressing the issue?<\/strong><\/p>\n

Airbnb is leveraging machine learning to optimize two processes as below:<\/p>\n

    \n
  1. Customer search<\/strong>: \u201cInitially, search rankings were determined by a set of hard-coded rules based on very basic signals, such as the number of bedrooms and price. And these rules were applied to every guest uniformly, rather than taking into account the unique values that could create the kind of a personalized experience.4<\/sup>\u201dAs Airbnb decided to scale the operations, it became an imperative to optimize this process. \u201cIn 2014, Airbnb created model to personalize search results for guests to easily discover homes. The company uses a machine-learned search ranking model to personalize results for guests. The model factors in guests’ tendencies to click on certain bookings. For example, Airbnb might look at whether customers favor specific types of d\u00e9cor in places they book. The company feeds more than 100 characteristics into the model, which then uses the data to identify patterns and personalize search rankings. It also uses host preferences to\u00a0personalize\u00a0search results for guests, promoting hosts likely to accept the accommodation request\u201d.2<\/sup><\/li>\n
  2. Pricing<\/strong>: Airbnb has heavily invested in machine learning to design dynamic pricing model i.e. offer price tips to the renters based on changing scenarios. It is highly critical for Airbnb to offer hosts an automated source of pricing information to come to a pricing decision in a time efficient manner. \u201cAirbnb hosts vary in their willingness to play concierge, cook, or tour guide. And events\u2014some regular, like seasonal weather changes; others unusual, like large local events\u201d5<\/sup>. Given the model must take in considerations multiple factors, pricing process can be optimized with machine learning processes. \u201cThe machine-learning pricing model uses historical travel patterns for the area to find the likelihood of any listing being booked at any time\u201d2<\/sup>. Using this inter-linkages and dependencies of data sets, model generates patterns that can be leveraged to predict right pricing for future host requests.<\/li>\n<\/ol>\n

     <\/p>\n

    What could they do differently going forward?<\/strong>\u00a0<\/strong><\/p>\n

    Short term<\/strong>: Airbnb should leverage machine learning in tackling inclusion challenges that it has been facing in recent future. Research says that guests tend to give lower ratings to the hosts if they belong to minority community, which in turn affects the future business of the hosts.6<\/sup> To calibrate these racial\/ community affects in the ratings, Airbnb should put race of the guest\/host as one of the filters and then based on historical data calibrate the ratings so that everyone is on the same level for future business.<\/p>\n

    Medium term<\/strong>: \u201cAs the platform expands into tours, activities, restaurant bookings, and concierge services, Airbnb\u2019s future growth will rely on their ability to offer guests ultra-personalized booking services\u201d2<\/sup>. Airbnb should try to leverage machine learning to model guests experience over their customer lifetime cycle. Machine-learning led modeling could use data sets such as age, geography, socio-economic status etc. \u2013 values for these data sets will change over time for an individual and hence Airbnb will be able to give personalize services that will differentiate it from the competitors.<\/p>\n

    Questions to consider<\/strong><\/p>\n

      \n
    1. Can machine learning help Airbnb in tackling regulation challenges that it\u2019s facing in recent future, such as \u2018One house One Guest\u2019 model?<\/li>\n
    2. How can Airbnb leverage machine-learning to design customized solutions for different economies?<\/li>\n<\/ol>\n

      (word count: 753)<\/p>\n

       <\/p>\n

      Endnotes<\/p>\n

        \n
      1. Sharan Shrinivasan, \u201cThe Medium.\u201dhttps:\/\/medium.com\/airbnb-engineering\/learning-market-dynamics-for-optimal-pricing-97cffbcc53e3<\/a>, accessed August 2010<\/li>\n
      2. Stephanie Pandolph, \u201cBusiness Insider.\u201dhttps:\/\/www.businessinsider.com\/machine-learning-is-driving-growth-at-airbnb-2017-6<\/a>, accessed Jun 2017<\/li>\n
      3. Robert Chang, \u201cThe Medium.\u201d https:\/\/medium.com\/airbnb-engineering\/using-machine-learning-to-predict-value-of-homes-on-airbnb-9272d3d4739d, accessed<\/a> July, 2017<\/li>\n
      4. VB Staff, \u201cThe VentureBeat.\u201dhttps:\/\/venturebeat.com\/2017\/06\/14\/airbnb-vp-talks-about-ais-profound-impact-on-profits\/<\/a>, accessed June 2017<\/li>\n
      5. Dan Hill, \u201cThe Secret of Airbnb\u2019s pricing algorithm.\u201d https:\/\/spectrum.ieee.org\/computing\/software\/the-secret-of-airbnbs-pricing-algorithm<\/a>, accessed August 2015<\/li>\n
      6. Deanna Tang, \u201cThe Skift.\u201d https:\/\/skift.com\/2016\/06\/22\/figuring-out-the-tech-solutions-to-airbnbs-racial-discrimination-problem<\/a>, accessed June, 2016<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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