/POC-New-scoring-model-for-ranking-cars-of-Drivy-search-Engine

The goal is to make a proof of concept of a new ranking algorithm for Drivy's search engine.

Primary LanguageJupyter Notebook

POC-New-scoring-model-for-ranking-cars-of-Drivy-search-Engine

The goal is to make a proof of concept of a new ranking algorithm for Drivy's search engine.

The goal of this test is to make a proof of concept of a new ranking algorithm for Drivy's search engine.

How does the search engine work?

  • Users input an address of search and desired dates and time for their trip. (screenshot)
  • Optionally, users can filter the results on various criteria such as the type of car (city, sedan, commercial vans..), price, options (AC, snow tires..) etc..
  • The search engine renders a list of cars. It follows 2 main steps to do so:
  • Filtering: Generate the list of cars that matches the search request: ie. Cars nearby the address, that are available for the dates of search, and that match the selected filters. This is not in the scope of this project.
  • Ranking: Order the list based on a score. Current score is computed with a simple heuristic. The scope of this project is precisely this ranking part.
  • Anytime the user changes a parameter, filter or page number, a new request is sent to the search engine and the results refresh.
  • Users can then visit different car listings, select cars and eventually book one of them.