User Attention Data Content Recommendation System
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Solution Overview
Problem
Current content recommendation algorithms fail to accurately assess user interest in media content due to their focus on content-specific behavior, neglecting multi-tasking behaviors during content consumption.
Innovation Solution
A system and method that collect user attention data, including content viewing information and simultaneous user activity, processed using a machine learning model to predict user interest, enabling more accurate content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content recommendation algorithms focus only on content-specific behavior, then the algorithm complexity remains manageable, but the accuracy of user interest assessment deteriorates
Solution Approach 1:
The system segments user behavior data into two distinct categories: content-specific behavior (watching patterns, completion rates) and multi-tasking behavior (device switching, app usage during content consumption). By processing these segments separately through different algorithmic pathways, the system captures comprehensive user interest signals while managing computational complexity through modular data handling.
Solution Approach 2:
The invention adds a new dimension to user behavior analysis by incorporating multi-tasking behavior data that occurs simultaneously with content consumption. This temporal and behavioral dimension complements the traditional content-specific behavior metrics, creating a more holistic user interest assessment model without requiring complete algorithmic redesign.
2Measurement precision
If the system collects and processes only content viewing data, then the data processing load remains low, but the accuracy of content recommendations deteriorates due to ignoring simultaneous user activities
Solution Approach 1:
The system segments data collection into two streams: content viewing data (from content delivery network) and multi-tasking behavior data (from device activity logs). Each stream is processed independently through specialized algorithms, allowing the system to handle increased data volume through parallel processing architectures rather than monolithic computation.
Solution Approach 2:
The patent introduces an intermediary processing layer that correlates content viewing events with simultaneous device activities. This intermediary layer acts as a bridge between raw data collection and final recommendation generation, filtering and synthesizing multi-source data before feeding it into the recommendation engine, thereby managing processing load effectively.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for deriving user attention data. In use, user attention data is collected for a user. The user attention data includes first information describing content being viewed by a user on a first device, and second information describing user activity occurring on the first device and/or one or more second devices while the content is being viewed by the user on the first device. Further, the first information and the second information are processed, using a machine learning model, to predict a degree to which the user likes the content. Still yet, the prediction is output for use in making one or more content recommendations.