Machine Learning Content Recommendation Platform
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Solution Overview
Problem
Users of linear content delivery services often face challenges in finding interesting content, leading to sub-optimal choices and wastage of resources due to non-personalized content recommendations, resulting in dissatisfaction and inefficient use of device resources.
Innovation Solution
A content recommendation platform utilizing machine learning to provide personalized electronic program guides by predicting user receptiveness and engagement duration for linear content, reducing resource wastage and improving user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static non-personalized content recommendations are provided to users, then the system complexity is reduced and ease of operation is improved, but user satisfaction deteriorates and resource wastage increases
Solution Approach 1:
The system automatically generates personalized content recommendations by analyzing user viewing history, preferences, and behavior patterns without requiring manual user input. The machine learning models self-adjust and refine recommendations based on accumulated data, enabling the system to serve itself in creating personalized experiences while reducing resource wastage through targeted content delivery.
Solution Approach 2:
The system dynamically changes recommendation parameters based on user behavior patterns, viewing history, and contextual factors. By adjusting recommendation algorithms and content selection criteria according to individual user profiles, the system provides personalized recommendations that improve user satisfaction while optimizing resource utilization through more accurate content matching.
2Reliability
If personalized content recommendations using machine learning are implemented, then user satisfaction is improved and resource efficiency is enhanced, but device complexity increases
Solution Approach 1:
The patent introduces intermediary components including machine learning models, data processing layers, and recommendation engines that act as mediators between raw user data and final content recommendations. These intermediary systems handle the complexity of personalization algorithms, allowing the core delivery infrastructure to remain relatively simple while still providing sophisticated personalized recommendations that improve user satisfaction and resource efficiency.
3Adaptability or versatility
If users manually search through electronic program guides to find interesting content, then content selection flexibility is maintained, but time consumption increases and resource efficiency decreases
Solution Approach 1:
The system performs preliminary actions by pre-analyzing user preferences, viewing history, and content characteristics to generate personalized recommendations before users need to make content selections. This advance preparation of tailored content suggestions reduces the time users spend searching through program guides while maintaining flexibility, as users receive curated options that align with their interests and can still choose from multiple personalized recommendations.
Data Source
AI summary
A device receives content data associated with a collection of content that is available to a user. The device determines, by processing the content data using a first data model that has been trained using machine learning, measures of likelihoods that the user will be receptive to each respective content of the collection of content. The device determines a duration during which the user is predicted to engage in a content watching session by using a second data model to process at least a portion of the content data. The device determines measures of utility that correspond to the collection of content and that measure utility that each respective content is predicted to have to the user during the content watching session. The device determines content recommendations for the user based on the measures of utility and causes at least one content recommendation to be displayed.