Location-Topic Model for Travel Snippet Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
User-generated travel content, such as blogs and wikis, is unstructured, biased, and lacks destination recommendations tailored to specific travel interests, making it difficult for consumers to plan trips effectively, while travel planning sites often rely on editorial content influenced by advertisers.
Innovation Solution
A technology that automates the mining of location-related aspects from travelogues using a probabilistic Location-Topic model for geo-snippet extraction and ranking, providing rich travel information by identifying relevant destinations and enriching travelogues with images, thus overcoming the limitations of unstructured and biased user-generated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated mining of location-related aspects from user-generated content is implemented, then the quality and relevance of travel information is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces a probabilistic Location-Topic (LT) model as an intermediary between raw user-generated content and travel recommendations. This model acts as a mediator that automatically extracts location-related aspects and topics from unstructured blog entries, transforming them into structured information without requiring manual intervention or complex editorial processes.
Solution Approach 2:
The system replaces manual editorial content creation and information verification with automated computational processes. The LT model and associated algorithms substitute human editors by automatically mining, analyzing, and ranking travel information from user-generated content based on location and topic relevance.
2Loss of information
If editorial content from travel planning sites is used, then the availability of travel information is improved, but the objectivity of the information deteriorates due to advertiser influence
Solution Approach 1:
The system enables user-generated content to serve itself by automatically extracting and organizing travel information from blog entries. The LT model allows the content to self-structure around location and topic aspects without requiring external editorial curation, thereby maintaining independence from advertiser influence while preserving information availability.
Solution Approach 2:
The patent extracts only location-related aspects and topics from user-generated content, separating relevant travel information from unrelated or biased content. This selective extraction process isolates objective location-based data from potential advertiser influence or personal biases in the original blog entries.
3Measurement precision
If manual analysis of user blogs is performed, then the accuracy of travel recommendations is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary automated analysis of user-generated content by pre-extracting location-related aspects and topics using the LT model. This preliminary processing organizes and structures travel information in advance, enabling rapid retrieval and recommendation generation without requiring time-consuming manual analysis when users seek travel advice.
Data Source
AI summary
Described herein is a technology that facilitates efficient automated mining of topic-related aspects of user-generated content based on automated analysis of the user-generated content. Locations are automatically learned based on dividing documents into document segments, and decomposing the segments into local topics and global topics. Techniques are described that facilitate automatically extracting snippets. These techniques include, for example, computer annotating travelogues with learned tags and images, performing topic learning to obtain an interest model, performing location matching based on the interest model, calculating geographic and semantic relevance scores, ranking snippets based on the geographic and semantic relevance scores, and searching snippets with a “location+context term” query.