Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning 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 paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!Comment: 8 page

The Use of Sequential Surveys to Shorten Implementation Time for Healthcare System-Level Enhanced Recovery After Surgery (ERAS) Pathways

Background Enhanced Recovery After Surgery (ERAS) pathways improve healthcare quality, safety, and cost-effectiveness. We hypothesized that the RAND Method (a hybrid Delphi approach), involving anonymous sequential surveys and face-to-face meetings, would allow for more rapid agreement and initiation of new ERAS pathways. Methods Using the ERAS Society guidelines for cesarean section as a baseline, our institution’s ERAS Leadership Team (ELT) compiled published literature and institutional practices to design a 32-component survey that was sent to obstetricians, nurse midwives, anesthesiologists, pharmacists, and nurses. Components that did not reach 90% consensus were included in a second survey the following week, and meetings were held to review results. At the conclusion of this process, time to agreement was retrospectively compared to the colorectal ERAS pathway process at this institution. Results ERAS pathway components were compiled and reviewed by 121 stakeholders at 7 hospitals using iterative surveys with review meetings over a 13-week period. Survey response rates were 61% and 50% in the initial and follow-up surveys, respectively. There was agreement on 28/32 and 32/32 items on the initial and follow-up surveys. Using the RAND Method, time to agreement decreased by 54.1% (24 vs 13 weeks) compared to prior system-wide efforts to standardize the colorectal surgery ERAS pathway. Discussion With rapidly expanding healthcare systems, effective methods to gain consensus and adopt ERAS pathways are critical to implementation of ERAS guidelines. We demonstrate that the RAND Method allows for a transparent and efficient means of agreement across a diverse group of clinicians practicing in several settings