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Recommendation algorithm for improving member experience and discoverability of resorts in the booking portal of a hotel chain

Recommendation algorithm for improving member experience and discoverability of resorts in the booking portal of a hotel chain

Goal:
Other
Problem addressed
Offering personalized recommendations by understanding the member
preferences from past holiday patterns and searches in the booking portal.
Various member and hotel features were also considered for the model.
Scope of use case
Building a personalized recommendation algorithm to help members of the hotel
chain to find their desired hotel for the family holiday.
Description
The traditional search engine in the member portal for
booking a hotel is mainly based on the members limited
visibility and knowledge of popular holiday destinations. In
contrast, a hotel chain can offer a variety of options to
members.
Each option brings a different holiday experience and
possibly include a lot of activities for family members to
choose from.
In the absence of an intelligent algorithm, many good hotels
would be invisible in the large number of hotels listed. This
would in turn also increase the burden on some popular
hotels that can get a disproportionally high number of
bookings, and sometimes run in overcapacity, depriving
other hotels of their share of bookings.
To solve this problem, the hybrid recommendation
algorithm helps shape the demand and brings up the hotels
that are similar to the ones a member has already visited
but yet provide a different experience, thus encouraging the
member to consider an alternative to their usual
preferences.
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