Conversational Item Search Refinement Through Multi-Facet Filtering
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Solution Overview
Problem
Current conversational product search systems are limited to handling single facet searches, requiring multiple queries and turns to refine search results, leading to inefficiency and inaccurate outcomes due to the inability to process multiple product facets and filter actions in a single utterance.
Innovation Solution
A multi-facet filter system that extracts multiple facets and actions from a single user utterance, applying them to a catalog to filter and score candidate items, reducing the need for multiple conversational turns and improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems are limited to single facet searches, then system complexity is reduced, but search accuracy and completeness deteriorate
Solution Approach 1:
The system segments the search query into multiple independent facets (e.g., brand, price, rating, color) and processes each facet separately through dedicated filter actions. This allows the system to handle complex multi-facet searches by breaking them down into manageable components, thereby improving search accuracy without overwhelming system complexity.
Solution Approach 2:
The system implements a universal filter manager that can handle multiple facet types and filter actions within a single query processing framework. By making the system multi-functional to accommodate various search refinement terms and facets, it improves search accuracy while avoiding the need for separate specialized systems for each facet type.
2Loss of time
If multiple separate queries are required for multi-facet search, then system complexity is reduced, but user time and interaction turns increase
Solution Approach 1:
The system merges multiple search refinement operations into a single query processing step. By combining multiple facet filters (brand, price, rating, etc.) into one unified search operation, the system eliminates the need for multiple separate queries and conversational turns, thereby reducing user time while managing query processing complexity through integrated architecture.
Solution Approach 2:
The system performs preliminary parsing and extraction of multiple facets and filter actions from the user's single query utterance before executing the search. This preliminary processing allows the system to prepare and execute multiple filter operations simultaneously, reducing the time required for search while managing complexity through advance preparation.
3Reliability
If systems recognize and apply limited types of facets and filter actions, then system complexity is reduced, but search result quality deteriorates
Solution Approach 1:
The system dynamically adjusts its facet recognition and filter application based on the specific parameters and terms present in each query. By adapting its behavior to match the specific facets and filter actions identified in the user's query, the system improves search result quality while managing complexity through parameter-driven flexibility rather than rigid pre-defined structures.
Data Source
AI summary
Examples provide conversational item search refinement using multi-facet filtering of items in a catalog. A multi-facet filter manager extracts facets and actions corresponding to the facets from a user utterance. The facet-actions include an entity-role and one or more filter actions associated with the facets. A facet-action includes filter actions such as exact, exclude, greater than, less than, etc. Multi-facet filters corresponding to the facet-actions are applied to a plurality of items in the catalog. The candidate items remaining after filtering are scored. The scores indicate relevance of each candidate item. One or more of the candidate items with the highest scores are selected. The selected items are added to search results which are returned to the user in response to the conversational search query. The multi-facet filter manager enables faster and more accurate search results using fewer conversational turns for reduced system resource usage and improved user efficiency.