Location Relevance Scoring for Booking Listings
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Solution Overview
Problem
Existing online booking systems rely solely on radial distance from a fixed reference point for ranking listings, which can lead to over or underweighting location significance, failing to accurately reflect user preferences due to the intangible value of real-world locations.
Innovation Solution
An online booking system that calculates a location relevance score incorporating city, neighborhood, and distance subscores, using geocoding and historical user behavior data to rank listings based on user intent, neighborhood popularity, and distance relevance, providing a more nuanced assessment of location desirability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If radial distance from a fixed reference point is used for ranking listings, then the ranking system is simple to implement, but the location significance is over or underweighted and fails to accurately reflect user preferences
Solution Approach 1:
The location relevance score is segmented into multiple subscores: city relevance subscore, neighborhood relevance subscore, and distance subscore. Each subscore evaluates a different aspect of location desirability independently, allowing the system to capture nuanced location preferences without requiring an overly complex monolithic model.
Solution Approach 2:
The system changes from a single parameter (radial distance) to multiple parameters (city relevance, neighborhood relevance, distance) that can be independently adjusted and weighted. This allows dynamic modification of location evaluation criteria based on user preferences and contextual factors.
2Productivity
If distance is the primary factor in ranking listings, then close listings are prioritized, but distance becomes less predictive when listings are merely a mile apart and fails to distinguish between locations with different intangible values
Solution Approach 1:
The location evaluation is segmented into hierarchical levels: city-level relevance, neighborhood-level relevance, and distance-level relevance. This segmentation allows the system to first filter by broad geographic match, then refine by local desirability, and finally adjust by proximity, providing accurate differentiation even among nearby listings.
Solution Approach 2:
The system adds dimensional depth to location evaluation by incorporating multiple subscores that evaluate different aspects (city, neighborhood, distance) rather than relying solely on the single dimension of radial distance. This multi-dimensional approach enables nuanced differentiation of location value.
Data Source
AI summary
An online booking system allows users to creates, search, and book listings of goods or services. When a user searches for listings, the listings are ranked at least in part based on a location relevance score including at least one of a city relevance subscore, a neighborhood subscore, and a distance subscore. Generally, the city relevance subscore quantifies the probability that a searching user may have actually intended to look for listings in a city other than the city specified in a search query. Generally, the neighborhood relevance subscore quantifies the popularity of specific neighborhoods within a city as a replacement or addition to the distance subscore that determines a real world distance between a listing's real world location and a location specified in a search query.