Search Ranking Module for Location Refining and Diversity
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Solution Overview
Problem
Existing online booking systems often skew rankings towards popular locations, leading to a lack of geographic diversity in search results, where listings from less popular regions are overshadowed by those from more popular areas, even if they are more relevant to the user's query.
Innovation Solution
The online booking system incorporates a ranking module that computes a location relevance score based on city, neighborhood, and distance relevance subscores, and includes diversification and refinement mechanisms to promote listings from various regions and adjust scores to ensure a diverse and relevant set of top-ranked listings, ensuring that listings from multiple regions are included and that those from more relevant areas are prioritized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If listings are ranked using radial distance from a reference point or city center, then the ranking reflects location-based relevance, but the top ranked listings become skewed towards popular locations with large numbers of listings, reducing geographic diversity
Solution Approach 1:
The system changes the ranking parameters by introducing multiple location relevance factors (city relevance, neighborhood relevance, distance relevance) instead of using a single radial distance metric. This allows the system to balance location accuracy with geographic diversity by adjusting the weight of each parameter
Solution Approach 2:
The location relevance score is segmented into multiple independent subscores (city relevance, neighborhood relevance, distance relevance), each calculating a different aspect of location匹配. This segmentation allows the system to evaluate and balance different location factors separately before combining them into an overall ranking
2Reliability
If the ranking system prioritizes popular locations to ensure high-quality listings, then the reliability of top results improves, but listings from less popular but relevant regions are overshadowed, reducing the comprehensiveness of search results
Solution Approach 1:
The system introduces a neighborhood relevance factor that independently evaluates the relevance of less popular neighborhoods to the search query, preventing them from being overshadowed by popular locations. This parameter change ensures that relevant listings from any region are captured
Solution Approach 2:
The system uses an intermediary relevance calculation mechanism that acts as a mediator between popular location bias and query relevance. The neighborhood relevance subscore serves as an intermediary factor that can boost listings from less popular regions when they are highly relevant to the query
Data Source
AI summary
An online booking system allows users to search and book listings of goods or services. When a user searches for listings, the listings are ranked and scored based on a number of factors including the location and the price of the listing, the number and quality of reviews and the number of successful prior bookings. In some situations, the listing scores overly skew the top ranking results to a particular region. The listing scores may be modified to address this skewing of results. When diversity in search results is desired, the listing scores are modified such that the top ranking results that are located in a diverse set of regions. When granular relevance of search results is desired, the listing scores are modified such that the top ranking results are located in regions that are more relevant to the search than the region to which the results are skewed.