Event-Number Threshold User Classification for Real-Time Content Delivery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional user identification systems are inefficient, inaccurate, and inflexible in identifying client devices for personalized digital content, requiring significant resources and relying on rigid identification methods like IP addresses, leading to suboptimal performance in real-time content delivery.
Innovation Solution
The system employs an event number threshold to generate a user classification model by selecting minimum prior event users, utilizing behavioral data to efficiently and accurately identify target users without relying on rigid identification methods, enabling flexible and real-time content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional user identification systems analyze all available user profiles to identify target users, then user identification can be performed, but significant memory and processing power are required and real-time content delivery is compromised
Solution Approach 1:
The patent segments the user identification process into two distinct phases: an offline training phase where a classification model is built using historical user profile data, and an online inference phase where the trained model rapidly identifies target users. This segmentation allows computationally intensive operations to occur offline while enabling fast real-time identification during content delivery, thus resolving the contradiction between identification reliability and delivery speed.
Solution Approach 2:
The system performs preliminary action by pre-processing and training the classification model offline before actual content delivery occurs. During the offline phase, the system analyzes user profiles and trains the model to recognize user characteristics. When real-time content delivery is needed, the pre-trained model can quickly identify users without requiring extensive processing, thereby enabling both accurate identification and fast content delivery.
2Ease of operation
If conventional user identification systems use rigid identification methods like IP addresses, then identification is straightforward, but the systems are inflexible and fail to identify users based on available digital characteristics
Solution Approach 1:
The patent transforms the identification approach by changing the parameters used for user identification. Instead of relying on fixed parameters like IP addresses, the system uses multiple variable parameters including user behavior patterns, device characteristics, and interaction histories. The classification model dynamically weighs these parameters to identify users, providing both simplicity through automated classification and flexibility through multiple identification dimensions.
Solution Approach 2:
The classification model serves as an intermediary between raw user data and user identification. Rather than directly comparing rigid identifiers, the model processes various digital characteristics as intermediate representations and synthesizes them into user identification decisions. This intermediary approach maintains operational simplicity while enabling flexible adaptation to different user characteristics and identification scenarios.
3Measurement precision
If conventional user identification systems require large amounts of computer memory and processing resources, then comprehensive user analysis can be performed, but system efficiency and resource utilization deteriorate
Solution Approach 1:
The patent extracts the computationally intensive user profile analysis operations and moves them to an offline training phase. The classification model learning process, which requires analyzing comprehensive user profiles, is performed offline when resource availability is not constrained by real-time delivery requirements. During online content delivery, the system only needs to apply the pre-trained model, which consumes minimal computing resources, thus achieving both accurate analysis and efficient resource utilization.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for providing customized digital content to a client device of a target user by generating and utilizing an event-number-specific user classification model. For example, the disclosed systems can train a user classification model based on a set of minimum prior event users who each satisfy a particular event number threshold. The event number threshold can correspond to a number of events that, when implemented by the target user identification system, cause the user classification model to converge. Upon training, the disclosed systems can detect an event associated with the client device of the target user, utilize the trained event-number-specific user classification model to identify the client device corresponding to the target user, and provide customized digital content to the client device of the target user.