Dynamic Filter Ranking for Search Interfaces
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Solution Overview
Problem
Current online shopping systems face challenges in providing and updating filters efficiently, as the number of available filters is vast and user-specific, making it difficult to meet individual user needs, and manual maintenance is impractical due to dynamic filter interactions and exposure of user intent.
Innovation Solution
A system and method that extract keywords from user inputs, determine relevant filters, rank them based on usefulness scores, and dynamically update filter rankings and availability, allowing users to select and apply filters while maintaining an optimized subset for search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a full list of available filters is provided for all products, then users have access to all possible filtering options, but the system becomes too complex to maintain and update efficiently
Solution Approach 1:
The system automatically generates and updates filter recommendations by analyzing user search behavior and product data, eliminating the need for manual maintenance of filter lists. The system serves itself by continuously learning from user interactions to optimize filter relevance.
Solution Approach 2:
The filter list is dynamically generated based on current search results and user behavior patterns rather than being static. The system adapts filter recommendations in real-time based on what users are searching for and how they interact with results.
2Reliability
If manual maintenance of filters is performed, then filter relevance can be controlled, but the process becomes impractical due to the large number and dynamic nature of filters
Solution Approach 1:
The system automatically maintains filter relevance by analyzing user search patterns and product characteristics, replacing manual maintenance processes. It self-adjusts filter priorities and selections based on real-time data without human intervention.
Solution Approach 2:
The system continuously monitors user interactions with search results and filter selections, using this feedback to automatically adjust and optimize filter recommendations. This closed-loop approach ensures filters remain relevant without manual updates.
3Measurement precision
If filters are applied to narrow down search results, then search specificity improves, but user intent may be exposed and filter applicability changes dynamically
Solution Approach 1:
The system dynamically adjusts filter applicability and visibility based on user interactions and search context. As users apply filters, the system automatically updates which filters are relevant and how they are presented, adapting to changing search conditions in real-time.
Data Source
AI summary
Methods and system for generating a filter interface in response to a user search are disclosed. The method includes extracting keywords from a search term, determining a plurality of filters associated with the keywords, determining a ranking for each of the plurality of filters, providing and displaying, based on the ranking, a subset of the plurality of filters as exposed filters, receiving a user selection of one of the exposed filters, in response to the second user input, applying the one of the plurality of filters and updating the ranking of the remaining of the plurality of filters, updating, to the first user, the search result, and regenerating the plurality of filters. The methods and system further includes methods to determine the ranking and filter arrangements on the user interface, and methods to update filters.