Intelligent Selector Controls for Context-Aware User Recommendations
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Solution Overview
Problem
Existing selector controls in user interfaces often require users to enter extensive search characters to retrieve relevant results, especially in large databases, and fail to provide accurate recommendations based on context, leading to inefficient user experience.
Innovation Solution
An intelligent selector control system that utilizes machine learning and collaboration features to predict and rank user selections based on user interactions and context, providing a list of most relevant results without extensive searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search-based selector controls are used, then users can access the database, but users must enter extensive search characters to retrieve relevant results
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing machine learning features and collaboration scores for all users in advance. When a search request is made, the system retrieves pre-computed features and applies the machine learning model to generate ranked recommendations immediately, eliminating the need for users to manually search through large databases and significantly reducing search time while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual search process with an automated machine learning-based recommendation system. Instead of requiring users to type search queries and manually review results, the system uses machine learning models that automatically analyze user behavior patterns, collaboration history, and contextual information to generate intelligent recommendations, substituting manual search mechanics with automated intelligent selection.
2Measurement precision
If traditional selector controls are used, then the interface is simple, but the system fails to provide accurate recommendations based on context
Solution Approach 1:
The system segments the complex recommendation problem into distinct modular components: feature extraction modules that process different types of data (user behavior, collaboration history, contextual information), a machine learning model that processes these features, and a ranking module that generates recommendations. This segmentation allows each component to be developed, optimized, and maintained independently, managing overall system complexity while achieving high recommendation accuracy through specialized processing of different data dimensions.
Solution Approach 2:
The patent introduces machine learning features as intermediary representations that bridge raw data and final recommendations. Instead of directly processing complex user behavior patterns and collaboration networks, the system extracts and represents these patterns as standardized machine learning features, which then serve as input to the recommendation model. This intermediary layer simplifies the system architecture by decoupling data processing from recommendation generation, making the system more manageable while improving recommendation accuracy.
3Productivity
If extensive searching is required, then complete search coverage is achieved, but user experience efficiency deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions with recommendations (selections, views, or rejections) are fed back into the machine learning model to continuously refine and improve recommendation accuracy. This feedback loop enables the system to learn from actual user behavior patterns and adapt its recommendations over time, increasing productivity by providing increasingly accurate results that require less user review time while maintaining complete search coverage through the underlying comprehensive database.
Data Source
AI summary
Methods and systems for intelligently recommending selections for a selector control are disclosed. The method includes receiving a recommendation request from a selector control client, the recommendation request comprising a search string and a unique identifier of a user interacting with a selector control; identifying user identifiers of usernames matching the search string; retrieving machine learning features corresponding to the user identifiers of usernames matching the search string; applying a machine learning model to the retrieved machine learning features to assign weights to the retrieved machine learning features; computing recommendation scores for the user identifiers based on the assigned weights to the retrieved machine learning features; ranking the user identifiers based on the recommendation scores; and forwarding a ranked list of user identifiers to the selector control client for displaying in the selector control for selection by the user interacting with the selector control.