User Profile Clustering for Video Streaming Personalization
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Solution Overview
Problem
Conventional video content recommendation systems fail to provide personalized recommendations for individual users within a single shared account, as they lack the ability to distinguish between multiple users' preferences due to the absence of individual profiles, resulting in recommendations that may not apply to all users.
Innovation Solution
The system generates individual user profiles by clustering video content items based on shared characteristics using neural networks and machine learning algorithms, analyzing streaming history data to identify affinity scores and provide customized recommendations for each user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional recommendation algorithms are used with a single shared account, then the system can provide recommendations based on combined viewing history, but the recommendations will not be personalized to individual users within the household
Solution Approach 1:
The patent segments the single shared account's viewing history into multiple individual user profiles by analyzing viewing patterns, timing, and content preferences. The system divides the aggregate data into distinct user segments (e.g., adult1, adult2, child1) based on behavioral clustering, enabling personalized recommendations for each segment while maintaining the shared account structure.
2Adaptability or versatility
If multiple user profiles are created within a single account, then personalized recommendations can be provided, but the system complexity increases due to the need to distinguish between multiple users' preferences
Solution Approach 1:
The system automatically creates and manages user profiles without requiring manual intervention. It uses machine learning algorithms to self-segment the viewing history and automatically generate profile characteristics, affinity scores, and content preferences. This eliminates the need for manual profile setup while providing personalized recommendations.
Solution Approach 2:
The patent transforms the data representation by introducing new parameters such as affinity scores, profile characteristics, and segmented viewing history attributes. These parameter changes enable the system to differentiate between users mathematically without adding visible complexity to the user interface or account management structure.
3Measurement precision
If viewing history is analyzed without clustering algorithms, then the system can process data quickly, but it cannot identify hidden associations between content items that would enable accurate user preference detection
Solution Approach 1:
The system performs preliminary clustering and organization of viewing history data into structured profiles before generating recommendations. By pre-processing the data into segmented user profiles with identified characteristics and affinity scores, the system prepares the information in advance, making subsequent recommendation generation faster and more accurate.
Data Source
AI summary
A computer-implemented method includes grouping video title items into clusters based on video title characteristics. A system processor identifies a connecting characteristic of the video title characteristics that associates a first cluster of video title items and a second cluster of video title items. The system creates a user profile associated with a video content account based at least in part on a history of streamed/downloaded video title items by identifying a video title characteristic associated with two or more video content items streamed/downloaded by the video content account, and grouping the two or more video content items based at least in part on the first cluster of video title items and the second cluster of video title items. The system provides a system-generated user profile that identifies user specific video content from an account having multiple users streaming content in association with the account.