Webpage Customization via Multi-Dimensional Context Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for user experience customization are one-dimensional, failing to provide multi-dimensional context analysis and personalized outputs based on community user activity, leading to sub-optimal user interactions.
Innovation Solution
An automated, community-driven, self-learning system that uses multi-dimensional input to customize user experiences by selecting page types, widgets, and configurations based on user activity feedback, employing a predictive model and perturbation engine to generate relevant content and introduce new selections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional one-dimensional systems are used for user experience customization, then system complexity is reduced, but user engagement and personalization quality deteriorate
Solution Approach 1:
The system segments user experience customization into multiple independent dimensions including user attributes, product attributes, contextual information, and behavioral patterns. Each dimension can be independently analyzed and customized, allowing the system to handle complex personalization tasks through modular processing rather than monolithic analysis.
Solution Approach 2:
The patent transitions from one-dimensional customization (single attribute-based filtering) to multi-dimensional customization by incorporating user demographics, product characteristics, contextual factors, and temporal patterns simultaneously. This dimensional expansion enables comprehensive personalization that adapts to diverse user needs across multiple facets of their experience.
2Adaptability or versatility
If multi-dimensional context analysis is implemented, then user engagement improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user data, product data, and contextual information in structured formats before actual customization requests. User profiles, product catalogs, and contextual databases are prepared in advance, enabling rapid retrieval and analysis when personalization is needed, thus reducing real-time processing delays.
Solution Approach 2:
The system implements feedback mechanisms that learn from user interactions and continuously refine customization models. By analyzing user responses to personalized content and adjusting algorithms based on engagement patterns, the system improves processing efficiency over time, reducing the computational burden of multi-dimensional analysis through optimized models informed by actual user behavior data.
Data Source
AI summary
A system and method for orchestration of customization for a user experience is disclosed. The system in an example embodiment includes automatically producing user experience customization selections for generating a webpage based on context information and a collection of user activity feedback from a community of users who previously interacted with the webpage. The user experience customization selections include a plurality of modules for inclusion in the web page, where each of the modules represent a user-interface element. One or more of the plurality of modules having input and output properties defining at least one application programming interface (API). The system in a further embodiment includes discovering dependencies between the one or more of the plurality of modules by extracting dependency information from the at least one API associated with the one or more of the plurality of modules. The system in another embodiment includes generating a dependency graph for the webpage based on the dependencies between the one or more of the plurality of modules and invoking the plurality of modules based on the dependency graph to generate the webpage.