How does the system select and rank properties when serving personalized recommendations?
Aidaptive selects and ranks inventory based on guest behavior, preferences, and real-time market conditions. The goal is to drive higher conversions, increased direct bookings, and better guest experiences by serving the most relevant options.
Data Collection & Guest Profiling
The engine continuously gathers and processes data from multiple sources, including:
- Guest behavior: Searches, clicks, time spent on listings, abandoned bookings.
- Past bookings: Previous stays, preferred room types, loyalty data.
- Contextual signals: Device type, location, time of day, seasonality, and real-time travel trends.
- Market trends & demand signals: Room availability, website traffic patterns, pricing.
Machine Learning & Heuristic Models for Personalization
The recommendation engine uses a combination of machine learning (ML) models and heuristic models to generate personalized recommendations.
These models learn patterns from guest behavior and inventory data to predict the most relevant listings:
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Collaborative Filtering
- Suggests inventory based on similar users' behaviors.
- Example: "Guests who booked this downtown single family home also looked at these nearby options."
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Content-Based Filtering
- Matches guest preferences with listing attributes (e.g., ocean view, pet-friendly, breakfast included).
- Example: If a guest prefers beachfront condominium properties, the system prioritizes those.
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Context-Aware Models
- Adapts recommendations based on seasonality, booking window, or location.
- Example: Last-minute travelers see listings with immediate availability aligned with their preferences.
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Popularity-Based Heuristics
- Prioritizes properties that are trending based on recent bookings and searches.
- Example: "This beachfront property has been booked 10 times in the last 30 days."
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Demand Surge Detection
- Identifies listings with growing interest (e.g., due to an upcoming event or holiday).
- Example: A city-center property sees a spike in demand due to a conference, so AI promotes it to guests searching within those dates.
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Exceptional Value for Cost
- Surfaces listings with the best price-to-value ratio, factoring in guest reviews, amenities, and discounts.
- Example: A 4-star property with a flash sale might be highlighted over a similarly priced 3-star option.
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Inventory Balancing Heuristics
- Dynamically adjusts recommendations to optimize room occupancy and revenue.
- Example: If a property has excess availability for a specific season, the AI might prioritize it in recommendations.
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Guest Lifetime Value (LTV) Optimization
- Personalizes recommendations based on a guest’s long-term revenue potential.
- Example: High-value repeat guests might see recommendations for premium properties.