Welcome to the Add-on recommendation feature page. This page serves as a comprehensive guide to understanding and implementing this new addition to Maxxton Software.

🚀 Overview

The add-on recommendation model is designed to enhance the booking experience by offering personalized suggestions for add-ons, tailored to each unique reservation. Utilizing machine learning algorithms, this feature analyzes guest data to provide real-time, relevant add-on recommendations during the booking process.

On top of that, add-on recommendations serve as a potential upselling technique through the Template Editor and My Environment.

Table of contents

• 🪄 How it Works

• 🕹 Enabling Add-on Recommendations

  • Reservation Manager

  • Web Manager

  • Template Engine

• 🛠 Implementation

• 🧐 Why do we recommend item X?

• ❓Questions

🪄 How it Works

The add-on recommendation model uses machine learning to personalize additions to a booking. It analyses and generalises past bookings and contextual information such as booking subjects, dates and length-of-stay, and accommodation information.

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Cold add-ons are extra services or products that no guest has ever booked before. These could be new offerings, but also updates of old products, like wine package 2023 getting renewed into wine package 2024. Because there’s no past booking data for these add-ons, it’s harder for the recommendation engine to know what type of guest wants them. We have two solutions for this problem:

Add-on linking

Coming back to the wine packages - some clients create new add-ons every year for roughly the same item. The booking data for both these add-ons is also the same. To address this, we developed an add-on linker that connects archived add-ons with their relevant data to highly similar, never-booked add-ons. This creates data for the cold add-ons and allows the model to recommend theses add-ons to fitting reservations.

Since there is little descriptive data available for add-ons, we link through the similarity of add-on names. This makes sure that wine packages will be connected, but a wine package and a BBQ package will not.

Random recommendations

For cold add-ons that cannot be linked to archived data, we have implemented random recommendations. This approach randomly inserts cold add-ons into the recommendation result. This approach helps gather initial booking data for these never-before-booked add-ons. By randomly suggesting these to guests and hoping they get booked, we can assess what type of reservation fits the add-on.

🕹 Enabling Add-on Recommendations

Reservation and Front Office Manager

Add-on recommendations are enabled by default and are in a separate list next to Favorite add-ons. If no add-ons can be recommended, the list will be hidden automatically.

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Web Manager

This implementation is

Template Engine

This implementation is

🛠 Implementation

There are two different implementations. One uses an existing reservation through the reservation service, and the second uses subjects through the resource service.

Both endpoints return a list containing add-ons and their probabilities. Both have the same two parameters:

• size - influences the amount of recommendations that return. This defaults to 4 when left empty.

• showColdStarts - When this showColdStarts = true, 25% of the request size will be random cold starts. This defaults to false.

Reservation Service

The endpoint for the reservation service is /api/v1/reservations/{reservationId}/reservedresources/{reservedResourceId}/addons/recommendations