Vector Engine for Activity-Based Media Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current media distribution platforms face challenges in scalability, accuracy, and efficiency when recommending media for specific activities due to the reliance on human curation, which is impractical for large catalogs and not personalized to individual user tastes.
Innovation Solution
A vector engine is trained using metadata from media object lists, titles, and interaction data to generate feature vectors, allowing for the selection of media based on cosine similarities, enabling automated and personalized media recommendations for designated activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human curation is used to manually tag media for activities, then accuracy of media recommendations is improved, but scalability is worsened due to the impracticality of manually analyzing millions of media items
Solution Approach 1:
The patent replaces the mechanical manual curation process with an automated machine learning system. The system uses neural networks to automatically analyze audio features, metadata, and user interaction patterns to determine media suitability for activities, eliminating the need for human editors to manually tag each media item while maintaining high accuracy through computational analysis
Solution Approach 2:
The patent transforms the curation process from qualitative human judgment to quantitative parameter analysis. The system extracts measurable parameters such as audio features (tempo, energy, danceability), metadata characteristics, and interaction patterns, then uses these numerical parameters to automatically determine media-activity suitability through machine learning models
2Adaptability or versatility
If human curation is used to create activity-based media lists, then personalization to user tastes is improved, but efficiency is worsened due to the time-consuming nature of manual analysis
Solution Approach 1:
The system enables self-service personalization by automatically analyzing user interaction data and media characteristics without human intervention. The machine learning model processes user listening patterns, skip rates, and engagement metrics to automatically generate personalized activity recommendations, eliminating the time-consuming process of manual curation while maintaining adaptability to individual user tastes
Solution Approach 2:
The patent performs preliminary analysis of media items by pre-extracting audio features, metadata, and interaction patterns during data collection. This preliminary processing enables the system to quickly generate personalized recommendations without requiring real-time manual analysis, significantly improving efficiency while maintaining personalization accuracy
3Reliability
If manual tagging of media is performed, then quality of activity recommendations is improved, but device complexity is worsened due to the need for comprehensive media analysis capabilities
Solution Approach 1:
The patent segments the complex media analysis task into distinct modular components: audio feature extraction, metadata parsing, interaction pattern detection, and machine learning classification. Each component handles a specific aspect of analysis independently, making the overall system more manageable and maintainable while ensuring comprehensive quality through coordinated processing of multiple data types
Data Source
AI summary
Media content is recommended based on suitability for a designated activity. A vector engine is trained using a plurality of lists, each of the lists containing metadata associated with a plurality of media objects. The vector engine includes a neural network trained with corpus data including (i) the plurality of lists (ii) a plurality of titles, each one of the titles associated with one of the lists, and (iii) the metadata associated with the plurality of media objects. Training the vector engine involves initializing, using the vector engine, a plurality of feature vectors representing each of the lists, each of the media objects, and each of a plurality of words in the titles of the lists. The training then further involves nudging, using the vector engine, the feature vectors based on a plurality of co-occurrences of the lists, the media objects, the words in the titles of the lists, or a combination thereof. A feature vector corresponding to an activity is identified among the feature vectors. At least one of the media objects, (ii) at least one of lists or (iii) a combination thereof suitable for the activity is selected based on cosine similarities between the feature vector corresponding to the activity and others of the feature vectors.