Media Recommendation via Image Analysis and User Metadata Filtering
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Solution Overview
Problem
Existing media recommendation systems fail to accurately suggest media items that align both with the content of an image and a user's historical consumption patterns, often resulting in a mismatch between image-based suggestions and user preferences.
Innovation Solution
A method and system that analyzes images to obtain relevant media items from a database, filters these items based on user metadata to ensure alignment with user history, and provides personalized media recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If media items are selected based solely on image analysis, then media items corresponding to image content are obtained, but the items may not align with user preferences and history
Solution Approach 1:
The patent combines image analysis results with user metadata filtering by merging the first plurality of media items (selected based on image content) with the second plurality of media items (selected based on user history and preferences). This integration ensures that the final media recommendation aligns both with the image content and the user's personal preferences, resolving the contradiction between image-based accuracy and user preference alignment.
2Reliability
If media items are filtered based on user metadata, then user preference alignment is improved, but the connection to image content may be weakened
Solution Approach 1:
The system merges two filtered sets of media items: those matching the image content and those matching user preferences. By combining these two result sets and selecting from their union, the system maintains both image content relevance and user preference alignment simultaneously, rather than allowing one filter to override the other.
3Device complexity
If a single filtering criterion is used, then the recommendation process is simple, but the accuracy of recommendations decreases
Solution Approach 1:
The recommendation process is segmented into distinct stages: first filtering media items based on image content analysis, then filtering based on user metadata, and finally merging the results. This segmentation allows each filtering criterion to be applied independently and systematically, improving overall recommendation accuracy while maintaining a structured, manageable process complexity.
Data Source
AI summary
The present disclosure relates to a method of providing media to a user based on analysis of an image. The method comprises analysing the image to obtain image information about what is depicted therein. The method also comprises, based on said obtained image information, selecting a first plurality of media items comprising audio, from a media database, said media items of the first plurality being associated with that which is depicted in the image according to the image information. The method also comprises filtering the first plurality of media items based on metadata associated with the user to obtain a plurality of seed media items. The method also comprises providing at least one media item from the media database to the user based on the obtained seed media items.