Dynamic Filter Suggestions for Search Interfaces
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Solution Overview
Problem
Current search systems face inefficiencies in narrowing down large result sets, making it difficult for users to quickly find desired search results, as they lack effective tools to rank and filter results based on user needs.
Innovation Solution
Implementing dynamic filter suggestions that are updated and ranked based on user selections, utilizing a machine-learning algorithm with Learning to Rank (LTR) models, which provides context-aware suggestions across multiple filter categories, improving the ranking of search results and user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple search query interface is used, then the system is easy to operate, but the user cannot efficiently narrow down large result sets
Solution Approach 1:
The patent segments the search result filtering process into multiple filter categories (e.g., job type, location, experience level, industry) with hierarchical structures. Each category can be independently expanded and filtered, allowing users to systematically narrow down results without overwhelming the interface.
Solution Approach 2:
The filter suggestions are dynamically updated based on user selections and search context. As users apply filters, the system recalculates and presents new relevant filter suggestions, making the interface adaptive rather than static.
2Device complexity
If static filter suggestions are provided, then the system is simple to implement, but the suggestions are not context-aware and less relevant
Solution Approach 1:
The system implements feedback loops where user interactions with filters (selections, expansions, collapses) are monitored and used to update filter suggestions in real-time. This creates a responsive system that adapts to user behavior patterns.
Solution Approach 2:
The patent changes the parameters of filter suggestions based on search context, user preferences, and interaction history. Filter suggestions are recalculated with updated parameters reflecting the current search state, making them more relevant to user needs.
3Loss of energy
If manual filtering of search results is required, then the system requires minimal processing, but users spend excessive time sorting through results
Solution Approach 1:
The system performs preliminary actions by pre-calculating and presenting relevant filter suggestions before users need them. Filter categories and options are organized and ready for user selection, reducing the cognitive load and time required for filtering.
Solution Approach 2:
The system serves itself by automatically generating, updating, and refining filter suggestions based on search queries and user interactions without requiring manual curation or configuration, enabling scalable automation.
Data Source
AI summary
Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.