Response Prediction System for Dynamic User Flow Optimization
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Solution Overview
Problem
Social networks face user drop-off issues when redirecting users to specific pages, with certain actions being less likely to occur on certain pages, leading to suboptimal engagement and business value.
Innovation Solution
Implementing a machine-learning model to predict the likelihood of user actions and redirect users to the most optimal page based on calculated business value and user engagement, using a response prediction system that considers user profiles, flow sequences, and potential actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users are redirected to specific pages after actions, then the system can guide user flow, but user drop-off increases and engagement decreases when the redirected page is not optimal
Solution Approach 1:
The patent applies dynamics by making the redirected page dynamic rather than static. The system determines the optimal page to redirect users to based on real-time factors including the type of action performed, user profile, flow sequence, and predicted likelihood of desired actions. This dynamic adaptation ensures each user receives a personalized landing page that maximizes engagement and minimizes drop-off, resolving the contradiction between guiding user flow and maintaining user engagement.
Solution Approach 2:
The patent changes parameters by adjusting multiple variables including page selection, timing, and content presentation based on predicted user behavior. The system modifies the landing page parameters (which page to present, when to present it, and what content to include) based on calculated business value and user engagement metrics, thereby optimizing both productivity and reliability.
2Productivity
If the system redirects users to pages with high business value actions, then business value increases, but user engagement may decrease if the page is not suitable for the user
Solution Approach 1:
The patent applies local quality by tailoring the redirected page to match the specific user and action context. Instead of a one-size-fits-all approach, the system selects pages and presents content that are locally optimized for each user's profile, action type, and flow sequence. This ensures the page is both high-value for the business and suitable for the individual user, resolving the contradiction between business value and ease of operation.
Solution Approach 2:
The system dynamically adjusts the page content and selection based on real-time user characteristics and action context. The predicted likelihood of desired actions is calculated and used to optimize the landing page in real-time, ensuring both high business value and good user engagement for each specific user-action combination.
3Measurement precision
If the system uses complex prediction models to determine optimal pages, then accuracy of user action prediction improves, but device complexity increases
Solution Approach 1:
The system applies self-service by having the prediction model automatically learn from user behavior patterns and flow sequences without requiring manual programming of complex rules. The model self-adjusts based on observed data, calculating predicted likelihoods of desired actions and automatically determining optimal pages. This reduces the need for manual system complexity while maintaining high prediction accuracy.
Solution Approach 2:
The patent changes parameters by using calculated business value and engagement metrics as inputs to the prediction model. Instead of using fixed complex rules, the system dynamically adjusts prediction parameters based on real-time data including user profiles, action types, and flow sequences, achieving high accuracy without proportional increases in system complexity.
Data Source
AI summary
A method and system to present an optimum action in response to a flow of actions in a computer network from a user are provided. For each of a plurality of possible presented actions corresponding to a particular flow of actions in a computer network, and for each of one or more possible performed actions for each possible presented action, a likelihood that a user will perform the possible performed action is determined. Then each of the determined likelihoods is weighted by applying a weight assigned to a corresponding possible presented action. An optimum presented action is identified determining a presented action having a weighted maximum determined likelihood, based on the weighted determined likelihood.