Venue Visit Inference Using Semantic Ranking
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Solution Overview
Problem
Existing positioning systems, such as those using GPS, Wi-Fi, or GSM, often fail to provide accurate location data in areas with poor signal strength or high venue density, making it difficult to determine specific venues visited by users, especially indoors or in urban areas, requiring explicit user selection for check-in.
Innovation Solution
A system that infers venue visits by supplementing location data with semantic information, ranking candidate venues based on user and venue characteristics, and optimizing rankings over sequences of visits to provide accurate venue identification and tailored content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based positioning is used to determine user location, then location data can be obtained, but accuracy deteriorates in areas with poor signal strength such as indoors or urban canyons
Solution Approach 1:
The system segments the location determination process into multiple independent components: GPS positioning, Wi-Fi positioning, and cellular tower triangulation. Each component operates independently to provide location data, and their results are combined to achieve reliable venue identification even when individual methods fail in challenging environments
Solution Approach 2:
The system merges multiple positioning technologies (GPS, Wi-Fi, cellular) with semantic information from social media check-ins and venue databases. This combination creates a hybrid location determination system that compensates for the weaknesses of individual methods, maintaining high accuracy and reliability in both open and challenging environments
2Measurement precision
If explicit user selection (check-in) is required to identify venues, then venue identification can be achieved, but user convenience deteriorates
Solution Approach 1:
The system implements self-service by automatically inferring venue visits using processed location data and semantic information from social media check-ins. The system processes location data, identifies candidate venues, and determines visited venues without requiring explicit user action, while maintaining high accuracy through multiple data sources and validation mechanisms
Solution Approach 2:
The system uses feedback from social media check-ins and user interactions to continuously refine venue identification accuracy. By analyzing patterns in explicit check-ins and comparing them with inferred visits, the system learns to improve automatic venue recognition, reducing false positives and enhancing convenience over time
3Measurement precision
If multiple candidate venues are considered in high density areas, then venue selection accuracy can be improved, but system complexity increases
Solution Approach 1:
The system performs preliminary filtering of candidate venues using multiple criteria before final selection: proximity to user location, operational hours matching visit time, category relevance, and historical check-in patterns. This preliminary action reduces the number of candidate venues from potentially dozens in high-density areas to a manageable few, simplifying the subsequent selection process while maintaining high accuracy
Data Source
AI summary
A method for inferring venue visits using semantic information includes receiving sensor data from sensors. An indication of a location is received that is associated with a user and determined based on the sensor data. A set of candidate venues associated with the location is determined based on the indication of the location. Sets of semantic information associated with the set of candidate venues are determined based on the sensor data. Candidate venues of the set are ranked by confidence that a given candidate venue corresponds to a visited venue of a venue visit based on the set of semantic information associated with the given candidate venue and additional semantic information associated with the user. A highest ranked candidate venue is selected as the visited venue and an indication is provided to a service causing content to be presented to the user based on the selected visited venue.