Affinity-Based Digital Photo Ranking System
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Solution Overview
Problem
Users of social networking platforms face difficulties in finding and viewing digital photographs of interest due to the large volume of photos shared by their connections, often requiring extensive searches and encountering irrelevant content.
Innovation Solution
A method for ranking digital photographs based on affinity scores that represent user relationships, where scores are determined by explicit and implicit interactions, such as tagging, co-occurrence in photos, and communication frequency, to provide users with curated lists of photos from their social circles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users conduct many searches to find photographs of interest, then they can access more photographs, but the time and effort required increases significantly
Solution Approach 1:
The system pre-calculates and stores affinity scores for all user pairs and photograph-user associations before search operations. When a user logs in, the system has already prepared ranked lists of photographs based on these pre-computed affinity metrics, eliminating the need for real-time calculation during user searches and significantly reducing search time while maintaining access to the full photograph catalog.
2Quantity of substance
If the system presents all photographs from social circles, then users see more content, but the relevance and interest level of photographs decreases
Solution Approach 1:
The system applies different quality levels of filtering and ranking to different portions of the photograph dataset. Photographs are ranked using affinity scores that reflect the strength of relationship between the user and people in the photographs. The system presents photographs in descending order of affinity, ensuring that the most relevant photographs (those with highest affinity scores) appear first, while still providing access to the full range of photographs from the user's social circles.
3Measurement precision
If the system conducts extensive analysis of user relationships and photo associations, then the accuracy of photo ranking improves, but the computational complexity and processing time increases
Solution Approach 1:
The system performs complex affinity score calculations in advance, before users need to view photographs. Affinity scores between users and photographs are pre-computed based on relationship data, photo metadata, and interaction history. These pre-computed scores are stored and updated periodically or when relevant data changes, avoiding the need to perform complex analyses in real-time during user sessions.
Solution Approach 2:
The system automatically maintains and updates affinity scores and ranked photograph lists without requiring manual intervention. When new photographs are uploaded or user relationships change, the system self-adjusts the affinity calculations and re-ranks photographs accordingly, reducing the operational complexity of managing large-scale photo databases with millions of users and photographs.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for affinity based ranking and display of digital photographs. In one aspect, a method includes receiving an identifier that identifies a first user; accessing data defining relationships of the first user to second users; accessing digital photographs; determining digital photographs that are each associated with one or more of the first user and the second users; for each digital photograph, generating a photograph score based on the association with one or more of the first user and the second users and corresponding affinity scores representing the relationships of the first user to the second users; ordering the digital photographs according photograph scores; and providing one or more of the digital photographs to a user device of the first user according to the order.