Hierarchical Booking Probability Prediction Using Cluster-Listing Models
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Solution Overview
Problem
In online marketplaces, determining the probability of booking for unique services across different dates is challenging due to the uniqueness of each listing and the lack of controlled pricing, making it difficult to generate accurate demand curves and optimize revenue.
Innovation Solution
The use of machine learning methodologies to analyze features of similar listings and generate cluster-level and listing-level probabilities of booking, combining these to provide a final probability for each service on a given date.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to analyze each unique listing individually, then booking probability prediction accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the prediction task into two hierarchical levels: (1) cluster-level prediction where listings are grouped into clusters with similar characteristics and a single probability is predicted for each cluster, and (2) listing-level prediction where individual listing probabilities are adjusted based on specific listing features. This segmentation reduces the overall computational burden while maintaining prediction accuracy for individual listings.
Solution Approach 2:
The patent combines cluster-level predictions with listing-level adjustments to produce final booking probabilities. By merging the broad patterns captured at the cluster level with the specific characteristics of individual listings, the system achieves accurate predictions without the computational cost of analyzing each listing in complete isolation.
2Measurement precision
If machine learning models analyze all listing features comprehensively, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments feature analysis into cluster-level features (shared by multiple listings) and listing-level features (unique to each listing). By pre-processing and analyzing cluster-level features once for groups of similar listings, the system avoids redundant computation while maintaining comprehensive feature analysis for accurate predictions.
3Adaptability or versatility
If individual pricing is allowed for each unique service, then service manager flexibility and revenue optimization potential are improved, but difficulty in determining booking probability and generating demand curves increases
Solution Approach 1:
The patent applies local quality by allowing individual listing predictions to be customized based on specific listing features while benefiting from general patterns learned at the cluster level. Each listing receives a tailored probability prediction that reflects its unique characteristics, enabling service managers to optimize pricing for individual services while the system manages the complexity through hierarchical processing.
Data Source
AI summary
Systems and methods are provided for extracting a plurality of features for a listing from a datastore comprising a plurality of listings and a plurality of features for each of the plurality of listings, determining a cluster of similar listings to the listing and generating a set of cluster features for the cluster of similar listings, analyzing the set of cluster features for the cluster of similar listings based on a booking price, using a first trained machine learning model to determine a cluster-level probability of booking the listing on the given date, analyzing the plurality of features for the listing using the booking price, using a second trained machine learning model to determine a listing-level probability of booking the listing on the given date, and generating a final probability of booking by combining the cluster-level probability of booking and the listing-level probability of booking.