Relevance-Based Search Refinement with Dynamic Guidance Attributes
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Solution Overview
Problem
Conventional search systems provide inefficient search refinement interfaces due to predefined attributes that may not be applicable to the search query, leading to cumbersome user experiences and limited refinement capabilities.
Innovation Solution
Implementing a relevance-based search refinement system with selectable guidance attributes, which are ranked based on historical user interactions, and a guidance-attribute control with embedded selectable values to facilitate efficient refinement of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard predefined attributes are provided in search refinement interface, then the interface structure is simple and easy to implement, but the attributes may not be applicable to the specific search query, leading to cumbersome user experience and limited refinement capabilities
Solution Approach 1:
The search refinement interface dynamically adapts its attributes based on the specific search query and historical user interactions. Instead of using static predefined attributes, the system generates context-relevant attributes in real-time, allowing the interface structure to change according to user needs while maintaining ease of use through automated attribute selection
Solution Approach 2:
The system pre-processes search queries to identify relevant attributes before presenting the refinement interface to users. By analyzing the search context and historical data in advance, the system prepares a tailored set of attributes that are most likely to be useful, reducing the cognitive load on users while providing highly relevant refinement options
2Productivity
If a predefined set of attributes is used in search refinement, then the implementation is straightforward, but the refinement options are not applicable to the search being performed, limiting effectiveness
Solution Approach 1:
The system incorporates feedback loops that analyze user interactions with search results and refine attribute recommendations in real-time. By monitoring which attributes users actually use and which search patterns emerge, the system continuously improves its attribute selection algorithm, making refinement options increasingly accurate and reducing user time investment
Solution Approach 2:
The search refinement system automatically identifies and presents relevant attributes without requiring users to manually search through predefined options. The system serves itself by using its own search data and historical patterns to generate context-appropriate refinement attributes, eliminating the time users would spend searching for applicable filters
3Ease of operation
If standard refinement features provide a derived set of attributes or attribute values, then comprehensive coverage is achieved, but the interface includes several inapplicable attributes that make it cumbersome
Solution Approach 1:
The system extracts only the most relevant attributes from the comprehensive set of available attributes based on the specific search context. By filtering out inapplicable attributes and presenting only those that are likely to be useful for the current query, the system maintains ease of operation while preventing information overload and user confusion
Data Source
AI summary
Methods, systems, and computer storage media for processing search queries using relevance-based search refinement are provided. In response to a search query, search result items are displayed on a search interface along with selectable guidance attributes. The guidance attributes are an identified ranked set of characteristics of items based on historical user interactions of users interacting with the search result items provided in response to the search query. Upon selection of a guidance attribute, a guidance-attribute control having embedded selectable values is displayed. A selection of an embedded value is received to cause execution of an embedded-value search operation. A first embedded-value search operation is executed to identify a subset of the items using the selected value. Alternatively, a second embedded-value search operation is executed to provide a dynamically updatable count of search results items that will be provided upon refinement of the search results items using the selected value.