Content Recommendation Profile Evolution via Time-Filtered Action Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current content recommendation systems fail to accurately account for various factors influencing user preferences, leading to irrelevant content suggestions, as they do not effectively filter or weight user actions such as brief channel changes or interruptions during content consumption.
Innovation Solution
A machine system that includes a monitor to detect user actions, a timer to apply a minimum time period filter, a sequencer to resequence events, and a combiner to transform user actions into aggregate actions, evolving a user profile based on weighted user actions, thereby improving content recommendation relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system records all user actions including brief channel changes and interruptions, then the user profile becomes more comprehensive, but the memory and bandwidth consumption increases
Solution Approach 1:
The patent extracts and removes insignificant user actions from the data stream by applying a time-based filter. Actions occurring within a threshold time period (e.g., channel changes during content playback) are identified and excluded from profile evolution, retaining only significant actions that truly reflect user preferences.
Solution Approach 2:
The system changes the parameter of time by introducing a time threshold as a filtering criterion. User actions are evaluated based on their temporal relationship to content consumption events, and actions occurring within the threshold period are discarded, thereby reducing data volume while preserving profile accuracy.
2Productivity
If the system processes every user action in real-time, then the recommendations are more responsive, but the computational complexity and resource usage increase
Solution Approach 1:
The system extracts and removes unnecessary processing operations by filtering out insignificant user actions before they enter the profile evolution pipeline. This reduces the number of actions requiring real-time processing while maintaining responsiveness to meaningful user behavior.
Solution Approach 2:
The patent applies preliminary filtering based on time thresholds before the main processing occurs. By pre-identifying and eliminating insignificant actions early in the pipeline, the system reduces the computational burden on subsequent processing stages while maintaining real-time responsiveness to significant events.
3Adaptability or versatility
If the system includes all user actions in profile evolution, then the recommendations cover more user behavior patterns, but the recommendation relevance decreases due to noise from insignificant actions
Solution Approach 1:
The system extracts and removes noisy user actions that do not reflect true user preferences. By filtering out actions occurring within threshold time periods (such as accidental channel changes during content playback), the system preserves accurate detection of genuine user preferences while maintaining coverage of meaningful behavior patterns.
Solution Approach 2:
The system introduces a time parameter threshold to differentiate between significant and insignificant user actions. Actions occurring within the threshold period relative to content consumption are excluded, thereby improving the precision of preference detection while still capturing broader behavior patterns through the temporal dimension.
Data Source
AI summary
A machine system for operating a content recommendation system includes a monitor to detect and identify user actions on network connected digital devices, a first associator to relate the user actions to digital content, a timer operable to control a minimum time period filter on the user actions, a sequencer operable in conjunction with the timer, the minimum time period filter, and a combiner to transform the user actions into aggregate user actions. The content recommendation system is responsive to the aggregate user actions to evolve a computer-stored user profile according to the aggregate user actions.