Personalizing Content Density via Machine Learning Gap Sensitivity
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Solution Overview
Problem
Current approaches to delivering content items on web platforms do not consider user 'blindness' to certain types of content, leading to decreased engagement and monetization, as they solely rely on relevancy and value without adjusting the density of content items based on user interest.
Innovation Solution
A machine-learning model is used to personalize the density of content items by introducing a minimum gap value and personalized gap sensitivity values, adjusting the spacing of content items based on user interaction data to prevent content item blindness, and implementing a PID controller to dynamically adjust the gap value based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content providers send additional content items to users through a content delivery service, then the quantity of content items delivered increases, but user engagement decreases due to content item blindness
Solution Approach 1:
The system applies different content delivery strategies to different users based on their individual characteristics and interaction history. Each user receives a personalized content density that optimizes engagement for that specific user, rather than applying a uniform delivery approach to all users. This is achieved through the content delivery service analyzing user attributes and adjusting content item density accordingly.
Solution Approach 2:
The system dynamically adjusts the density parameter of content items based on user interaction data and performance metrics. By changing the density parameter - controlling how many content items of a particular type are displayed - the system optimizes user engagement while preventing content blindness. This involves monitoring interaction metrics and adjusting content delivery parameters in response to observed user behavior.
2Quantity of substance
If too many content items of a particular type are displayed at one time, then the quantity of content items increases, but users begin to ignore those content items completely
Solution Approach 1:
The system implements periodic monitoring and adjustment of content item density based on user interaction feedback. By continuously observing user behavior patterns and periodically adjusting the density of content items displayed, the system prevents users from developing blindness to content items. This involves setting thresholds for content density and adjusting them based on observed engagement metrics.
Solution Approach 2:
The system uses user interaction data as feedback to adjust content delivery strategies. By monitoring how users interact with content items - including signs of ignoring or blindening to certain content types - the system adjusts the density and distribution of future content items. This feedback loop ensures that content delivery remains optimized for engagement while preventing content blindness.
Data Source
AI summary
Techniques for using a machine-learned model to personalize content item density. In one technique, an entity that is associated with a content request is identified. Multiple sets of content items are identified that includes content items of different types. A first position of a first slot is determined in a content item feed that comprises multiple slots. A second position of a previous content item is determined, in the content item feed, that is of a first type. A difference between the first position and the second position is determined. Based on the difference, a gap sensitivity value that is associated with the entity and is different than the difference is determined. Based on the gap sensitivity value, a content item from the multiple sets of content items is selected and inserted into the first slot. The content item feed is transmitted to a computing device to be presented thereon.