Personalized Image Recommendations via User Profile Matrices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image search engines fail to provide personalized image results that closely match a user's interests due to their reliance on generalized database searches, often excluding relevant images that have not been recently edited, leading to suboptimal thumbnail results.
Innovation Solution
The method involves generating personalized image recommendations by creating a recommendation matrix based on user profile information and collaborator profiles, using a trained bag of visual words model and word mover's distance algorithm to calculate weighted recommendation scores for candidate images, ensuring that images are selected and displayed that closely match the user's area of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional generalized database searches are used, then the system can service queries for millions of users with billions of images, but the search results are not personalized and exclude relevant images that have not been recently edited
Solution Approach 1:
The system pre-generates user profiles by analyzing user interactions, search history, and saved images before search queries are submitted. This preliminary action creates ready-to-use personalized data structures that enable rapid customization of search results without adding complexity to the real-time search process
Solution Approach 2:
The patent introduces user profiles as an intermediary layer between the general image database and search results. These profiles act as mediators that translate generic search queries into personalized results by filtering and ranking images based on stored user preferences and interaction patterns
2Ease of operation
If the system uses last modified date to classify image search results, then it can provide a simple classification method, but it excludes images that are more relevant to a particular user
Solution Approach 1:
The system changes the classification parameter from objective metadata (last modified date) to subjective user-specific parameters (user profile preferences, interaction history, saved images). This parameter change enables the same simple classification mechanism to produce highly accurate, personalized results without increasing operational complexity
3Productivity
If generalized searches are used to service image queries, then the system can handle large volumes of user queries, but it produces suboptimal thumbnail results that fail to match user interests
Solution Approach 1:
The system enables queries to self-adapt to user preferences through automatically generated and updated user profiles. The profiles continuously learn from user interactions and automatically adjust search results without requiring manual configuration or complex real-time processing, maintaining high query processing capacity while improving matching accuracy
Data Source
AI summary
One example method involves operations for receiving a query that includes a keyword. The search query is associated with a user profile. Operations further include a recommendation matrix that includes a set of images based on (a) an area of interest determined from the search query and the user profile and (b) content tags associated with the images. In addition, operations include calculating a recommendation score for a candidate image included in the recommendation matrix. The recommendation score includes a weighted average of row vectors of the recommendation matrix. Further, operations involve including the candidate image in a search result for the search query based on the recommendation score. Additionally, operations include generating, for display, a search result that includes the candidate image.