[AISWorld] RecTour 2024 challenge - are you up to it? (co-located with RecSys 2024)

[Tsvi Kuflik]

RecTour 2024 workshop (https://workshops.ds-ifs.tuwien.ac.at/rectour24/ )
challenges participants to rank accommodations' reviews

Description:

As a part of the RecTour workshop, the RecTour 2024 Challenge organized by
Booking.com will take place.

It focuses on ranking reviews, which is an important aspect that influences
users’ decision-making. The most trivial way to rank reviews would be
according to review scores or time-based.

An alternative approach would be to rank the reviews with the most
“helpfulness” votes. However, the main problem with this approach is that
most of the reviews do not get this helpfulness votes thus suffering from
presentation bias.

In this challenge, the task is to match given accommodations and users to
their respective review IDs. The concept is that when a new user interacts
with the booking system, we can analyze the accommodation they are viewing
along with available user features (e.g., couple, country, etc.). This
enables us to display reviews in an order that considers the review content
with respect to the user and accommodation characteristics.

To do so, Booking.com provides a unique training dataset containing 1.6
million reviews based on real anonymized bookings. 

Registration and participation:

In order to participate in Booking.com RecTour 2024 challenge each team need
to fill this form : https://forms.gle/wRvzGJeMg4roFH9u6 

Upon completion of your registration you will receive an email with the link
to the dataset and further instructions how to extract it.

 

Data:

There are 3 sets of data for this challenge. Currently only train data is
available. Later on as a challenge will progress we will release a
validation set and a test set as well.

Each set is separated into three files:

    Users – hold information regarding users and accommodation features.

    Review – hold information regarding reviews.

    Matches – a true label between given user_id accomodaiton_id and
review_id (only positive examples).

Matches file for the test set (the true label) –  won’t be accessible during
the competition, and will be used in order to assess submitted predictions.

Participants are encouraged to create their own negative labels by levering
the information from the Matches file.

For more details, see:

https://workshops.ds-ifs.tuwien.ac.at/rectour24/rectour-2024-challenge/

 

Tsvika

Tsvi Kuflik, PhD.

Professor of Information Systems,

  Information Systems department,

  The University of Haifa

  Email: tsvikak at is.haifa.ac.il

  Home page: https://tsvikak.hevra.haifa.ac.il

  Tel: +972 4 8288511

  Fax: +972 4 8288283