Content Recommendation via Vector Embedding and Mixture Model Clustering
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Solution Overview
Problem
Conventional content presentation systems that rely on genre labels fail to capture the real-life interconnectivity between users and content items, limiting the presentation of relevant content to users who often listen across genres, as they do not accurately reflect consumer preferences and actual groupings.
Innovation Solution
A method using collaborative filtering to generate vector representations of items and users, reducing them to a two-dimensional space via t-distributed stochastic neighbor embedding (t-SNE), and applying mixture models like Gaussian or Dirichlet process mixture models to create clusters and discrete categories, allowing for partial membership and consumption-driven categorization, which provides more accurate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional genre labels are used to organize content, then content can be categorized into groups for easy browsing, but the system fails to capture real user consumption patterns and interconnectivity between users and content across genres
Solution Approach 1:
The patent transforms the categorical parameter of genre labels into a continuous vector space representation. Each content item and user is represented as a vector in a multi-dimensional space, where similarity is measured by distance metrics rather than discrete category membership. This allows for nuanced, gradient-based similarity measurements that reflect actual consumption patterns while maintaining the organizational benefits of categorization.
Solution Approach 2:
The patent adds dimensional complexity by moving from one-dimensional genre labels to multi-dimensional vector representations. The collaborative filtering matrix generates vectors with multiple dimensions that capture various aspects of user-content interactions, enabling the system to represent and discover relationships that transcend traditional single-axis genre classifications.
2Adaptability or versatility
If collaborative filtering with vector representations and dimensionality reduction is implemented, then personalized recommendations can be provided based on actual consumption patterns, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent extracts the essential similarity relationships from the high-dimensional collaborative filtering data and represents them in a reduced two-dimensional space using t-SNE. This extraction process preserves the critical pairwise similarity information while eliminating redundant dimensions, significantly reducing computational complexity for recommendation generation while maintaining personalization accuracy.
Solution Approach 2:
The patent segments the complex recommendation problem into distinct processing stages: (1) generating collaborative filtering vectors from user-content interactions, (2) reducing dimensions to two-dimensional space, (3) applying mixture models to identify clusters, and (4) generating recommendations based on cluster membership. This segmentation allows each stage to be optimized independently and facilitates efficient computation at scale.
3Adaptability or versatility
If mixture models are used to create clusters and discrete categories, then content can be assigned to multiple categories with probability scores reflecting partial membership, but the computational overhead for determining category assignments increases
Solution Approach 1:
The patent performs preliminary clustering using mixture models during an offline training phase, pre-computing the cluster assignments and probability scores for all content items. When generating recommendations, the system only needs to retrieve and combine these pre-computed probabilities rather than performing full mixture model calculations in real-time, dramatically reducing the time required for category assignment during recommendation generation.
Data Source
AI summary
A method of recommending content items includes obtaining vector representations of items based on a matrix of items versus item users that indicates item use by the users, reducing each of the vector representations to a two-dimensional space, creating clusters having cluster centers using one or more mixture models based on the reduced vector representations, using the cluster centers for one or more mixture models to create discrete categories to which items can be assigned and providing one or more recommendations to a first user based on the item assignments within the discrete categories.