Social Event Recommendations via Graph Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current online event management and social networking systems lack effective integration of social graph data to personalize event recommendations, leading to suboptimal user engagement and event participation.
Innovation Solution
The integration of social graph data analysis within online event management systems to score friend connections and rank event listings based on user interests, social network information, and event history, allowing for personalized event recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If social graph data is integrated to personalize event recommendations, then user engagement and event participation increase, but system complexity and data processing requirements worsen
Solution Approach 1:
The system segments the recommendation process into distinct modules: social graph analysis component that processes friend connections, event history analysis component that processes user attendance patterns, and event ranking component that combines these analyses. This segmentation allows each component to handle specific data processing tasks independently, managing system complexity while enabling personalized recommendations that improve event participation
Solution Approach 2:
The patent introduces an intermediary recommendation engine that sits between the raw social graph data/event history data and the final event recommendations. This intermediary processes and scores friend connections and event histories, transforming raw data into meaningful recommendations without requiring the entire system to handle all data processing simultaneously, thus managing complexity while improving engagement
2Measurement precision
If social network information and event history are analyzed to rank events, then recommendation accuracy improves, but data processing time and computational resources worsen
Solution Approach 1:
The system performs preliminary analysis of social graph data and event history data in advance, scoring friend connections and event attendances before they are needed for recommendations. This pre-processing stores computed scores that can be quickly retrieved and combined during recommendation generation, improving recommendation accuracy while reducing real-time processing time
Solution Approach 2:
The patent changes the parameter representation from raw social graph data and event history records to computed scores (friend connection scores, event history scores). This parameter transformation condenses large volumes of raw data into compact numerical values that are faster to process and combine, maintaining recommendation accuracy while reducing computational time and resources
Data Source
AI summary
In one embodiment, a method includes accessing a plurality of event listings, accessing event information associated with the event listings, accessing social network information associated with a particular user, and ranking the event listings for the particular user based at least in part on the social network information and event information.