Negative Signal Probability for Content Selection
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Solution Overview
Problem
Existing content delivery systems face challenges in predicting user interest effectively, leading to negative user experiences and revenue losses due to content items being closed or ignored by users, as current methods lack accurate analysis of user interactions and behavior patterns.
Innovation Solution
A method is introduced to determine negative signal probabilities based on user interaction sequences, allowing for informed decisions on content presentation, where the sequence of actions on a client device is analyzed to predict the likelihood of content items being closed, and content selection is optimized to minimize negative signals by considering user profiles and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content items are automatically presented to users based on basic recommendation algorithms, then content delivery speed and volume are improved, but user engagement decreases and revenue is lost due to content being closed or ignored
Solution Approach 1:
The system performs preliminary analysis of user interaction sequences before content presentation to determine negative signal probabilities. By pre-processing user behavior data and calculating engagement risk metrics in advance, the system can predict which content is likely to be closed or ignored, thereby preventing revenue loss before it occurs while maintaining efficient content delivery
Solution Approach 2:
The system implements a feedback mechanism where user interactions with presented content are continuously monitored and fed back into the prediction model. The sequence of user actions is analyzed to update negative signal probabilities, allowing the system to adapt and improve content selection over time, thereby maintaining both delivery efficiency and engagement reliability
2Device complexity
If traditional content selection methods are used without analyzing user interaction sequences, then system complexity is reduced, but measurement precision of user interest prediction deteriorates
Solution Approach 1:
The system segments user interaction data into discrete action sequences that can be individually analyzed. By breaking down complex user behavior into manageable action units, the system can process and analyze interaction patterns without requiring excessive computational resources, thus maintaining reasonable system complexity while improving prediction accuracy through detailed sequence analysis
Solution Approach 2:
The patent introduces an intermediary layer that processes user interaction sequences and translates them into negative signal probabilities. This intermediary processing layer acts as a mediator between raw user actions and content selection decisions, enabling accurate user interest prediction without directly complicating the core content delivery system
Data Source
AI summary
One or more computing devices, systems, and/or methods are provided. In an example, a sequence of actions performed using a first interface on a first client device may be identified. A first negative signal probability may be determined based upon the sequence of actions. The first negative signal probability may correspond to a probability of receiving a negative signal associated with a first content item from the first client device responsive to presenting the first content item via the first interface on the first client device. The first interface on the first client device may be controlled based upon the first negative signal probability.