Personalized Module Layout Using Interaction Likelihood Prediction
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Solution Overview
Problem
Conventional techniques for arranging modules on a user interface result in fixed arrangements that do not adapt to individual user interactions, leading to computational inefficiencies, increased power consumption, and lost conversion opportunities due to the overwhelming volume of irrelevant information.
Innovation Solution
Implementing machine learning models to analyze user interaction data and generate personalized module arrangements based on interaction likelihood predictions, using techniques such as multi-head self-attention and triplet loss to refine user history representations and interaction predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional fixed arrangement techniques are used, then device complexity is reduced, but adaptability to user needs deteriorates
Solution Approach 1:
The patent implements dynamic module arrangement by using machine learning models to predict interaction likelihoods and reorder modules based on user behavior patterns. The system transitions from static fixed arrangements to dynamic personalized arrangements that adapt in real-time based on user interactions, history, and preferences.
Solution Approach 2:
The system performs self-service by automatically learning user preferences and generating personalized arrangements without requiring manual user input or configuration. The machine learning model continuously adapts to user behavior patterns autonomously, eliminating the need for users to manually adjust module arrangements.
2Ease of operation
If all modules are displayed to users, then information completeness is improved, but user engagement deteriorates due to overwhelming irrelevant information
Solution Approach 1:
The patent extracts and filters out irrelevant modules from the complete set of available modules using machine learning predictions. By calculating interaction likelihoods for each module and selecting only those above a threshold, the system removes unnecessary information while preserving relevant content, thereby reducing cognitive load for users.
Solution Approach 2:
The system applies local quality by personalized the arrangement based on individual user characteristics and preferences. Each user receives a customized module arrangement tailored to their specific interaction history and predicted preferences, rather than a uniform generic arrangement applied to all users.
3Productivity
If fixed module arrangements are used, then computational efficiency is maintained, but conversion opportunities are lost
Solution Approach 1:
The system performs preliminary action by pre-calculating interaction likelihoods for all modules using machine learning models before presenting them to users. By predicting which modules users are most likely to interact with based on historical data and patterns, the system prepares optimized arrangements in advance, improving conversion opportunities without significant computational overhead during actual user interaction.
Data Source
AI summary
In implementation of techniques for personalized module arrangement via machine learning, a system receives user interface modules and interaction data corresponding to one or more interaction sessions. Based on the interaction data and the user interface modules, the system generates one or more user history representations via a machine learning model. The system generates, based on the one or more user history representations, interaction likelihood predictions via the machine learning model, wherein each interaction likelihood prediction corresponds to a likelihood of interaction with at least one of the user interface modules. Based on one or more interaction likelihood predictions above a predefined threshold value, the system generates an arrangement of the user interface modules. The system broadcasts the arrangement of the user interface modules for display.