Venue Ranking Model for Accurate Check-in Recommendations
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Solution Overview
Problem
Existing location-based services, such as social networking platforms, rely on GPS geolocation accuracy, which can be unreliable in urban areas due to building interference, leading to inaccurate recommendations of nearby venues.
Innovation Solution
A data-processing system that trains a model using past check-in data to rank venues based on relevance, independent of proximity, considering user behavior, popularity, and contextual factors like time and venue characteristics, to provide more accurate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If venues are selected based on proximity to user geolocation, then the system is simple to implement, but the recommendation accuracy deteriorates when GPS is unreliable
Solution Approach 1:
The patent introduces an intermediary ranking system that mediates between the user's geolocation and the final venue recommendations. Instead of directly selecting venues based solely on proximity, the system uses a ranking mechanism that considers multiple factors including user behavior patterns, venue popularity, and contextual information. This intermediary layer filters and adjusts the raw proximity-based results to produce more accurate recommendations even when GPS data is unreliable.
Solution Approach 2:
The system dynamically changes the parameters used for venue selection based on the reliability of geolocation data. When GPS accuracy is low, the system shifts from relying primarily on spatial proximity to incorporating alternative parameters such as user check-in history, venue ratings, and contextual factors. This parameter adaptation allows the system to maintain recommendation quality despite deteriorating location data.
2Adaptability or versatility
If the system relies on GPS geolocation accuracy, then it can provide location-based services, but the reliability deteriorates in urban areas with building interference
Solution Approach 1:
The system prepares for potential GPS failures by pre-establishing alternative venue selection mechanisms. Before relying on geolocation data, the system has already built in fallback methods using user behavior patterns and venue metadata. This cushioning approach ensures that when building interference degrades GPS reliability, the system can seamlessly transition to using these pre-prepared alternative data sources without service interruption.
Solution Approach 2:
The system implements feedback loops that continuously monitor the quality and reliability of geolocation data. When the system detects that GPS accuracy deteriorates in urban environments, it uses this feedback to adjust its venue selection strategy, reducing reliance on proximity data and increasing weight on alternative factors such as user preferences and venue characteristics. This feedback mechanism enables dynamic adaptation to changing environmental conditions.
Data Source
AI summary
A technique is disclosed for presenting possible cheek-in venues to a mobile device user, such as for the purpose of checking in with social networking services such as Foursquare. The disclosed technique operates by training a model during an initial training sequence. The trained model, representing a teamed hypothesis, describes venues that are ranked according to their relevance to users who are also described by the model. During a subsequent run-time sequence, the system receives real-time geolocation data that represents users, wherein the data includes user geolocation and the level of accuracy of the geolocation. The system generates a set of candidate venues, ranks the generated candidate venues by applying the learned hypothesis, which can be dependent on the level of accuracy, and presents the user with the ranked venues as possible cheek-in venues. The user can then select the venue actually being checked into from the presented check-in venues.