Enhanced Interface Generation with Contextual Scoring
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Solution Overview
Problem
Current interface generation systems fail to accurately provide augmentation elements related to search queries or different contexts, and they often lack context explanations for the selection of these elements.
Innovation Solution
A system that includes a processor configured to receive a request for an enhanced interface, generate candidate items based on a query or an anchor item, and generate composite candidate scores using a pick item scoring engine. The system then selects top-k items, chooses pick items and enhancement interface elements, and transmits a response to generate a user interface with these elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current interface generation systems provide augmentation elements on an item basis, then interface elements are provided for items, but the systems are unable to accurately provide augmentation elements related to search queries or different contexts
Solution Approach 1:
The system segments the augmentation element selection process into multiple independent scoring components: a base score from historical interactions and context-specific scores from different scoring engines. Each scoring engine evaluates candidate items independently based on specific contextual factors, and these scores are then combined to form a composite ranking. This segmentation allows the system to maintain high contextual adaptability while achieving precise selection through aggregated evaluation.
Solution Approach 2:
The system dynamically changes evaluation parameters by selecting different scoring engines based on the detected context type. When a search query context is identified, query-relevant scoring engines are activated; when a category browsing context is detected, category-relevant scoring engines are used. This parameter change approach enables the system to adapt to different contexts while maintaining measurement precision through context-appropriate evaluation criteria.
2Ease of operation
If current systems present interface elements indicating popular items, then interface elements are displayed, but the reason for identification can be unrelated to the current context of the search query or unclear
Solution Approach 1:
The system implements feedback by generating human-readable explanations that communicate why specific items were selected for augmentation elements. The explanation generator analyzes the scoring results and context information to produce clear rationale statements (e.g., 'This item is recommended because it matches your search criteria and has high user engagement'). This feedback mechanism preserves context explanation information while maintaining ease of operation by presenting it in an understandable format.
Solution Approach 2:
The explanation generator acts as an intermediary between the complex scoring system and the user. It translates the technical scoring results and context data into clear, natural language explanations that bridge the gap between the system's internal evaluation process and the user's understanding. This intermediary preserves important context information without compromising the ease of operation.
3Measurement precision
If the system generates composite candidate scores using multiple historical interactions, then accurate item selection is achieved, but the computational complexity increases
Solution Approach 1:
The complex scoring system is segmented into multiple independent scoring engines, each responsible for evaluating candidate items based on specific historical interaction types. Instead of one monolithic complex scoring system, we have separate engines for different contexts (search queries, category browsing, item details) that can be independently developed, maintained, and optimized. This segmentation reduces overall system complexity while maintaining high scoring accuracy through specialized evaluation.
Solution Approach 2:
The scoring system is designed with universal components that serve multiple functions. The same base scoring engine and explanation generation framework are used across different contexts (search, category browsing, item details), reducing redundancy. Context-specific scoring engines are added modularly to handle different interaction types, allowing the system to maintain universality in its core architecture while achieving precision through context-aware extensions.
Data Source
AI summary
Systems and methods for generating enhanced user interfaces are disclosed. A set of candidate items responsive to a request for an enhanced interface is generated. The set of candidate items is responsive to a query or an anchor item. A composite candidate score for each candidate item is generated by a pick item scoring engine based on one or more historical interactions. A set of top-k items is selected from the set of candidate items and at least one pick item is selected from the set of top-k items. At least one enhancement interface element is selected for display in conjunction with the at least one pick item and a response to the request for the enhanced interface is transmitted that causes a receiving device to generate a user interface including the at least one pick item displayed in conjunction with the at least one enhancement interface element.