Geo-Sensitive Social Event Recommendation Engine
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Solution Overview
Problem
Mobile device users face difficulty in finding meaningful, location-specific events that align with their interests due to the vast amount of information available, making it hard to filter and recommend relevant events in real-time.
Innovation Solution
A mobile-based real-time geo-sensitive social event recommendation engine that analyzes data from sources like Twitter, Facebook, and Yahoo! Social Updates, using Natural Language Processing to extract and rank events based on user interests, location, and relevance, and provides personalized recommendations on a mobile device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If real-time data from multiple sources is analyzed to provide comprehensive event information, then the quantity and variety of events increase, but the difficulty of filtering and finding meaningful information increases
Solution Approach 1:
The patent extracts and isolates relevant event information from vast amounts of social media data using NLP techniques. The system identifies and extracts specific event entities, attributes, and relationships from unstructured text, separating meaningful event data from noise in the social media streams.
Solution Approach 2:
The patent introduces an intermediary recommendation engine that acts as a mediator between raw social media data and users. This engine processes, filters, and ranks events based on user profiles and preferences, transforming unstructured data into personalized event recommendations.
2Measurement precision
If location-based filtering is applied to provide geo-specific events, then the relevance of events to user location improves, but the complexity of the recommendation system increases
Solution Approach 1:
The patent applies local quality by tailoring event recommendations to specific user locations and contexts. The system processes social media data differently based on geographic location, providing location-specific event information that is relevant to each user's local area while maintaining a unified system architecture.
3Measurement precision
If personalized recommendations are generated based on user interests, then the relevance of recommended events improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing social media data as it arrives, organizing event information in advance for quick retrieval. User profiles and preferences are also maintained in ready-to-use formats, enabling fast matching when event recommendations are requested without extensive real-time computation.
Data Source
AI summary
Performing an on-line recommendation includes: analyzing real-time data from various sources; determining, from the analysis, events in which a user may be interested; extracting the determined events; storing the extracted events in a data store; and performing a recommendation function. The recommendation function includes: ranking the extracted events to determine the events in which the user is most likely to be interested; and performing location-based filtering, retaining those extracted events that are within a geo-location range proximate to the user, thus generating optimal events.