Search Refinement via Context-Aware Query Generation
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Solution Overview
Problem
Traditional search engines for enterprises rely on manual configurations and data entry, which are error-prone and lack natural language processing, requiring users to enter keywords for search queries, and are not optimized for current context or user behavior, especially on mobile devices with limited screen space.
Innovation Solution
Implementing a system that uses product attributes, user context, and machine learning to automatically optimize search results, allowing users to select 'more like this' options to find similar products without manual keyword entry, by generating search queries based on product descriptions, user behavior, and contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional search engines require manual keyword entry, then search functionality can be provided, but user operation complexity increases and search efficiency decreases
Solution Approach 1:
The search system automatically performs refinement operations without requiring user intervention. The system monitors user interactions with search results and autonomously generates refined search queries, eliminating the need for users to manually enter keywords or configure search parameters.
Solution Approach 2:
The system pre-configures refinement logic and search strategies based on historical data and user behavior patterns. By preparing refinement rules in advance, the system can quickly execute optimized searches without requiring users to think about or specify search parameters at the moment of querying.
2Reliability
If manual configurations are used for search engines, then implementation is straightforward, but error rates increase and reliability decreases
Solution Approach 1:
The patent replaces manual mechanical configuration processes with automated machine learning systems. Instead of users manually configuring search parameters and refining queries, the system uses algorithms to automatically analyze user behavior, generate refined search terms, and optimize results based on learned patterns.
Solution Approach 2:
The system continuously monitors user interactions with search results and uses this feedback to automatically refine future search queries. By incorporating real-time feedback loops, the system adapts to user preferences and improves search accuracy without requiring manual reconfiguration.
3Adaptability or versatility
If search results are optimized for desktop interfaces, then comprehensive information can be displayed, but mobile user experience deteriorates due to limited screen space
Solution Approach 1:
The search result presentation dynamically adapts to the user's device and context. The system adjusts the amount and format of information displayed based on screen size, device type, and user interaction patterns, providing optimized views for both desktop and mobile environments without compromising the comprehensiveness of search capabilities.
Data Source
AI summary
Techniques for search with more like this refinements are disclosed. In some embodiments, search with more like this refinements includes receiving a product and a context (e.g., the context can include related category information, user context, and/or other context related information); generating a search query based on the product and the context; and determining a plurality of products that match the search query to generate more like this search results.