Information Pushing via Clustering and Feedback
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Solution Overview
Problem
Existing information pushing methods are inefficient in accurately identifying and targeting potential users due to oversimplification of user characteristics, leading to inaccurate selection and reduced marketing effectiveness, as they lack proper explanation of user portraits and feedback mechanisms.
Innovation Solution
A method that analyzes seed populations by clustering individual characteristics, generates feature weight vectors for each cluster, and uses feedback to correct and improve targeting accuracy, expanding the marketing scope and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple screening methods are used to select target users based on basic browsing history, then the operation complexity is low and processing speed is fast, but the targeting accuracy is insufficient and marketing effectiveness deteriorates
Solution Approach 1:
The patent segments the user selection process into multiple stages: initial seed user selection based on simple criteria, followed by iterative diffusion to potential users, and continuous feedback-based optimization. Each stage operates with appropriate complexity level, avoiding the need for highly complex algorithms throughout the entire process.
Solution Approach 2:
The patent implements feedback mechanisms where marketing results are continuously monitored and used to adjust and optimize the user selection model. This feedback loop enables the system to improve targeting accuracy over time without requiring initially complex algorithms, as the model learns and adapts through accumulated data.
2Measurement precision
If comprehensive user characteristics analysis is performed to improve targeting accuracy, then the measurement precision improves, but the computational time and processing speed decrease
Solution Approach 1:
The patent performs preliminary actions by pre-selecting seed users based on simple but effective criteria (actual purchasers or collectors of relevant products). This preliminary selection establishes a high-quality starting point that enables accurate diffusion to potential users without requiring complex analysis of all users from the beginning.
Solution Approach 2:
The patent applies partial action by focusing computational resources on the most influential users (seed users and their direct connections) rather than analyzing all users equally. The diffusion process prioritizes users who are most likely to be converted, performing comprehensive analysis only where it provides maximum marginal benefit.
3Quantity of substance
If the marketing scope is expanded to reach more potential users, then the quantity of targeted users increases, but the accuracy of user selection may deteriorate
Solution Approach 1:
The patent implements a dynamic user selection process where the target user list is continuously updated and refined. As the diffusion progresses and feedback is received, the system dynamically adjusts which users are selected as targets, ensuring that accuracy is maintained even as the overall marketing scope expands to reach more potential users.
Data Source
AI summary
A server acquires a feature label vector of each seed user and forms a first number of clusters corresponding different information categories according to the feature label vectors of the seed users. The server calculates a central vector of each cluster according to the feature label vectors of the seed users in the cluster. The server acquires a feature weight vector corresponding to the information categories. The server acquires a feature label vector of each potential user. The server calculates first distances from the potential users to the central vector of the information categories according to the feature label vectors of the potential users, feature weight vectors and central vectors corresponding to the information categories. The server selects a second number of potential users corresponding to the shortest first distances from the first distances and sends them information that is matched with corresponding information categories of the target users.