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🪄 How it Works

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🕹 Enabling Add-on Recommendations

  • Reservation Manager

  • Web Manager

  • Template Engine

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🛠 Implementation

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🧐 Why do we recommend item X?

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❓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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  • 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.

🧐 Why do we recommend item X?

Good question! To put it simply: We don’t exactly know ourselves. The algorithm finds patterns, relations, and correlations in the data that aren’t immediately obvious to humans. It examines reservation profiles, booking histories, and other details to make these recommendations. While the exact reasoning can be complex and unclear, the algorithm’s ability to identify these hidden connections often results in surprisingly effective suggestions.

❓Questions

Q: Is this a replacement for Favorite add-ons?

A: It can be used as a replacement, but it also works well side by side.

Q: I would like to recommend item X more often than item Y. Is that possible?

A. We can influence the recommendation engine in multiple ways. Please contact someone from the Data Science team or check the contact information below.

🖨 Contact

Data Scientist, Developer

User Profile
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