RecSys Challenge 2019

The RecSys Challenge 2019 will be organized by trivago, TU Wien, Polytechnic University of Bari, and Karlsruhe Institute of Technology, and presents a real-world task in the travel metasearch domain. Users that are planning a business or leisure trip can use trivago’s website to compare accommodations and prices from various booking sites. trivago provides aggregated information about the characteristics of each accommodation to help travelers to make an informed decision and find their ideal place to stay. Once a choice is made the users get redirected to the selected booking site to complete the booking. It is in the interest of all participants (traveler, advertising booking site, and trivago) to suggest suitable accommodations that fit the needs of the user and have a high chance of a redirect (click-out).

The goal of this challenge is to develop a session-based and context-aware recommender system using various input data to provide a list of accommodations that will match the needs of the user.

In the challenge, participants will be tasked with predicting which accommodations (items) have been clicked in the search result during the last part of a user session in an offline evaluation setup.

A detailed description of the challenge can be found on the website of the RecSys Challenge 2019.
Accepted contributions will be presented during the RecSys Challenge 2019 Workshop.

About trivago

trivago is a global hotel search platform focused on reshaping the way travelers search for and compare hotels, while enabling advertisers of hotels to grow their businesses by providing access to a broad audience of travelers via our websites and apps. trivago has established 55 localized platforms in over 190 countries and provides access to over two million hotels, including alternative accommodations, with prices and availability from over 400+ booking sites and hotel chains. Our search platform makes sense of a complex, fragmented web of online hotel offerings in a comprehensive way, by aggregating information (reviews, ratings, images, …) from a deep supply of hotels and websites to paint the most precise picture of each hotel.

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