The Conference for Machine Learning Innovation

Using Machine Learning for Property Mapping in the OTA Industry

Shorttalk
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✓ 50% off on all prices
✓ 10% team discount
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Register until September 23:
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10 % Team Discount
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Join the ML Revolution!
Register until September 23:
✓ PS Classic or C64 Mini for free
✓ Save up to €310
10 % Team Discount
Register Now
Join the ML Revolution!
Register until the conference starts:
✓ 2-in-1 conference special
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Register until the conference starts:
✓ 2-in-1 conference special
✓ 10 % Team Discount
Register Now
Infos

This talk addresses an important problem in the OTA (Online Travel Agency) industry, namely property mapping. The problem asks for matching same property entities (hotels, rooms, etc.) across different domains and channels in order to ensure a consistent user experience on the platform. This task is traditionally performed manually by expert users who screen for salient information in the structured data such as property name, address, number of rooms, and other features. With the emerging business model of NHA (Non-hotel Accommodation) and the sharing economy, such as Airbnb and the like, the mapping problem is further convoluted, because as opposed to traditional hotels, most of the property information is user-generated (e.g. images, and natural language textual description) and highly unstructured. Thus, the manual process of mapping is further complicated and requires more time and effort from expert users. On the other hand, the task of automated mapping, which has been traditionally attempted by rule-based algorithms, becomes ineffective and erroneous with low accuracy and coverage. In this talk, I will briefly survey the efficient big data pipeline used at Agoda to automate the process of NHA property mapping, as well as outlining some of the important machine learning problems and solutions used in this pipeline.

This Session originates from the archive of Diese Session stammt aus dem Archiv von SingaporeSingapore . Take me to the program of . Hier geht es zum aktuellen Programm von Singapore Singapore .

This Session originates from the archive of Diese Session stammt aus dem Archiv von SingaporeSingapore . Take me to the program of . Hier geht es zum aktuellen Programm von Berlin Berlin .

This Session originates from the archive of Diese Session stammt aus dem Archiv von SingaporeSingapore . Take me to the program of . Hier geht es zum aktuellen Programm von Munich Munich .

This Session Diese Session originates from the archive of stammt aus dem Archiv von SingaporeSingapore . Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

Behind the Tracks