Location Attribute Extraction via Frequency Analysis
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Solution Overview
Problem
Travel booking systems often fail to present contextual and interesting content that effectively entices users to visit specific locations, as existing methods rely on generic terms that do not highlight unique attributes of destinations.
Innovation Solution
A system processes listings and reviews to identify descriptive attributes for locations by generating a corpus of words, calculating expected frequencies, and determining descriptiveness metrics, which labels certain words as attributes that are meaningful to users, allowing for the presentation of unique location characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If high frequency words from listings and reviews are used to describe locations, then the system can identify popular terms associated with the location, but the terms become too generic and fail to describe unique attributes that would entice travelers
Solution Approach 1:
The patent applies local quality by making the word selection process location-specific. Instead of using generic high-frequency words across all locations, the system calculates location-specific frequencies and compares them against expected frequencies to identify words that are locally distinctive. This allows each location to have its own unique set of descriptive attributes rather than sharing generic terms.
Solution Approach 2:
The patent changes the parameter of word frequency analysis from absolute frequency to relative frequency comparison. By calculating expected frequencies based on overall corpus distribution and comparing against location-specific frequencies, the system transforms the measurement parameter to identify words that are statistically significant for each location, thereby improving descriptiveness.
2Loss of information
If the system uses common terms like 'apartment' and 'Muni' that appear frequently in San Francisco listings, then it can provide contextual insights about the location, but these terms do not identify unique attributes that would motivate users to visit
Solution Approach 1:
The system applies local quality by identifying words that are locally distinctive to each location. Common terms like 'apartment' and 'Muni' that appear in many locations are filtered out, while location-specific terms with high local frequency ratios are selected. This ensures that the descriptive attributes are adapted to each specific location's unique characteristics rather than using generic terms.
Solution Approach 2:
The patent inverts the traditional approach by not selecting words based on their overall frequency alone, but by selecting words based on their relative frequency compared to expected distributions. Instead of asking 'how often does this word appear?', the system asks 'how much more often does this word appear here compared to elsewhere?', thereby inverting the selection criterion to prioritize uniqueness over popularity.
3Measurement precision
If the system processes all listings and reviews to identify location attributes, then it can provide meaningful descriptive labels, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing the corpus processing into location-specific segments. Instead of analyzing the entire corpus as a single unit, the system processes listings and reviews separately for each location, calculating location-specific frequency distributions. This segmentation allows for more accurate location-specific attribute identification while managing computational complexity through modular processing.
Solution Approach 2:
The system applies partial action by focusing computational resources on identifying the most significant descriptive attributes for each location rather than exhaustively analyzing every possible word. By using statistical thresholds and frequency ratios, the system identifies a sufficient subset of descriptive words without requiring complete enumeration of all potential attributes, thereby reducing processing complexity while maintaining accuracy.
Data Source
AI summary
Listings and reviews of listings can be processed to identify descriptive attributes for locations associated with the listings. To do this, a corpus of words is generated for various locations based on listings in the locations and reviews of those listings. An expected frequency, and per-location frequency for each word is determined. These numbers are in turn used to determine a number of high frequency listing locations, and a number of below expected frequency listing locations for each word. Based on a comparison of the number of high frequency listing locations and the number of below expected frequency listing locations of a word with an attribute reference number, the word can be identified either as an attribute that is likely descriptive of the location, or not.