Content Recommendation Model Selection Using User Interest Clustering
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Solution Overview
Problem
Existing content recommendation systems fail to adequately match content to users, leading to suboptimal content delivery.
Innovation Solution
A distribution platform utilizing an analytics subsystem that employs machine learning models to analyze user activity data and generate user interest clouds, selecting appropriate content recommendations based on clustering methods and recommendation types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation systems use user surveys and viewership statistics, then content recommendations can be generated, but content matching accuracy is insufficient
Solution Approach 1:
The patent transforms recommendation system operation by changing the fundamental parameters from aggregate statistics to individual user behavioral sequences. Each user's content consumption pattern is converted into a sequence representation, enabling precise matching through sequence similarity comparison rather than broad statistical categorization.
Solution Approach 2:
The patent creates a digital copy of each user's content consumption behavior as a sequence model. This sequence copy captures the temporal and contextual patterns of user interactions, allowing the system to match users with content based on replicated behavioral fingerprints rather than explicit feedback.
2Adaptability or versatility
If recommendation systems process individual user data, then personalization improves, but system complexity increases
Solution Approach 1:
The patent applies homogeneity by standardizing all user behavioral data into a unified sequence format. Regardless of the diversity of user actions (views, searches, interactions), each is transformed into a consistent sequence structure with standardized tokens, simplifying processing while maintaining personalization capability.
Solution Approach 2:
The sequence modeling approach serves multiple functions simultaneously: it captures user preferences, models behavioral patterns, enables similarity comparison, and supports real-time recommendations. This universal representation method reduces system complexity by eliminating the need for separate processing mechanisms for different recommendation tasks.
3Quantity of substance
If content platforms provide a plethora of content, then content variety increases, but recommendation effectiveness decreases
Solution Approach 1:
The patent replaces traditional mechanical recommendation approaches (keyword matching, category filtering, collaborative filtering) with a sequence-based neural network model. This substitution enables the system to handle vast content variety by understanding the semantic and contextual relationships in user behavior sequences, achieving precise matching even with diverse content libraries.
Data Source
AI summary
Methods, systems, and apparatuses for improved model selection and content recommendations are described herein. A distribution platform may comprise a system of computing devices, servers, software, etc., that is configured to present media assets (e.g., content) at user devices. In one example embodiment, an analytics subsystem may provide at least one content recommendation to a user device using a machine learning model. The machine learning model may be selected based on a clustering method using an unsupervised machine learning model.