Machine-Learned Database Interaction Model for Targeted Engagement
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Solution Overview
Problem
Centralized database systems struggle to effectively identify which entities are most likely to interact with presented content items, limiting the ability to provide targeted content recommendations.
Innovation Solution
A central database system trains a machine-learned model using entity and content item characteristics, along with interaction data, to identify a subset of entities most likely to interact with specific content items, and displays these items with interface elements that facilitate interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized database systems store and analyze large amounts of entity interaction data, then they can identify patterns and characteristics of entities, but they struggle to effectively identify which entities are most likely to interact with presented content items
Solution Approach 1:
The patent replaces traditional mechanical rule-based filtering systems with a machine learning model that automatically learns patterns from historical interaction data. The model substitutes manual or simple algorithmic entity identification with an intelligent system that processes entity characteristics, content item characteristics, and interaction history to predict engagement likelihood, thereby resolving the contradiction between data analysis capability and effective identification precision.
2Ease of operation
If the system presents content items to all entities in the target set, then all entities receive content, but the likelihood of meaningful interactions decreases due to lack of targeting
Solution Approach 1:
The patent applies local quality by differentiating content distribution strategies for different entity subsets. Instead of uniform content presentation to all entities, the system uses the machine learning model to identify high-probability interaction entities and targets content specifically to them. This localized targeting approach maintains operational simplicity while significantly improving interaction reliability by adapting content distribution to individual entity characteristics and predicted engagement likelihood.
3Device complexity
If the system uses traditional filtering methods to identify target entities, then the process is simple and fast, but the accuracy of identifying entities likely to interact with content items is low
Solution Approach 1:
The patent implements preliminary action by pre-training the machine learning model on historical interaction data before actual content recommendation. The system performs advance learning of patterns and characteristics from stored entity interaction data, building a trained model that can then quickly and accurately identify target entities. This preliminary training phase resolves the contradiction by establishing accurate identification capabilities beforehand, allowing the system to maintain both reasonable complexity and high precision during actual operation.
Data Source
AI summary
A central database system trains a machine-learned model based on training data identifying entity characteristics of account holder entities, content item characteristics of a content item presented to the account holder entities, and interactions between the account holder entities and the presented content item. The central database system then identifies a target set of account holder entities, and applies the trained machine-learned model to the entity characteristics of each account holder entity of the target set of account holder entities, the entity characteristics of each of the account holder entities that previously interacted with the content item, and the content item characteristics of the content item to identify a subset of the target set of account holder entities for presentation of the content item. The content item is then displayed to the subset, the content item includes an interface element that, when selected, causes an interaction to take place.