Social Network Place Ranking via User Comparison and Duplication Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online social networks face challenges in accurately determining user location, ranking places, and suppressing irrelevant entities in search results, due to complex social graphs and varying user interactions.
Innovation Solution
The system determines user location by analyzing geographic and social networking information, ranks places based on user comparisons and reviews, and suppresses entities by evaluating duplication values and social graph interactions to provide personalized and relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses complex social graph analysis to determine user location and rank places, then the accuracy of location determination and place ranking is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the complex social graph analysis into multiple independent components: location probability distribution calculation, entity duplication detection, and place ranking algorithms. Each component processes specific aspects separately, reducing overall system complexity while maintaining accuracy.
Solution Approach 2:
The patent introduces intermediary data structures such as location probability distributions and entity similarity scores that mediate between raw social graph data and final place rankings. These intermediaries simplify the computation by breaking down the complex analysis into manageable steps.
2Measurement precision
If the system analyzes extensive social graph information and user interactions to suppress irrelevant entities, then the relevance of search results is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by pre-calculating entity duplication values and location probability distributions before actual search queries. This allows the system to quickly suppress irrelevant entities during search operations without performing extensive real-time analysis.
Solution Approach 2:
The patent changes parameters by using similarity thresholds and confidence scores to filter entities. By adjusting these parameters, the system can efficiently suppress irrelevant entities without exhaustive analysis, balancing relevance with processing speed.
3Adaptability or versatility
If the system generates personalized place recommendations based on detailed user profiles and social network data, then the personalization quality is improved, but the data processing requirements and system resources increase
Solution Approach 1:
The system uses universal data structures and algorithms that serve multiple functions: location determination, place ranking, and personalized recommendations all utilize the same social graph analysis framework and entity comparison mechanisms, reducing redundant computational energy.
Data Source
AI summary
In one embodiment, a method includes, by one or more computing devices of an online social network, sending, to a client system of a first user of the online social network, a first request to compare two or more place-entities associated with the online social network, where the first user is connected to each place-entity within a social graph of the online social network, each place-entity being associated with a particular score on a first scoring scale and a first feature. The method further includes receiving, from the client system, comparison information responsive to the first request, the comparison information indicating an ordered ranking of the two or more place-entities. The method also includes accessing a scored list of place-entities associated with the online social network, where the scored list is based on scores on the first scoring scale for the place-entities.