Interactive Search Query Refinement for Intent Accuracy
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Solution Overview
Problem
Existing internet search systems fail to accurately determine user intent from short search queries, leading to time-consuming and frustrating searches, and often disseminate user data without consent.
Innovation Solution
A computer-implemented system with a collaborator unit that utilizes a reference data database and search term database to analyze user inputs, refine queries, and provide targeted search results by considering user profile data, publisher data, and product data, while minimizing data dissemination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines process short search queries using basic algorithms, then the search system remains simple and fast to operate, but the search accuracy and ability to determine user intent deteriorates
Solution Approach 1:
The system segments the search process into multiple stages: initial query processing, iterative refinement cycles with collaborative filtering, and result generation. Each stage handles specific aspects of query understanding, allowing the complex task of intent determination to be broken down into manageable components that improve accuracy without overwhelming system complexity
Solution Approach 2:
The system performs preliminary actions by pre-processing user profile data, search history, and contextual information before the actual search query is submitted. This preparatory work enables the system to quickly refine short queries into more accurate search terms without adding significant delay to the user experience
2Measurement precision
If search engines collect and analyze extensive user data to improve search results, then search accuracy improves, but user data privacy and security deteriorates
Solution Approach 1:
The system introduces an intermediary layer that processes user data through collaborative filtering algorithms and contextual analysis without directly exposing raw personal information. This intermediary mechanism allows the system to leverage user profile data and search history for improved accuracy while maintaining privacy through indirect processing and aggregation techniques
Solution Approach 2:
The system implements feedback loops where search results and user interactions continuously refine the understanding of user intent without requiring extensive collection of new personal data. This feedback mechanism improves search accuracy over time by learning from interaction patterns rather than relying solely on extensive user data collection
3Measurement precision
If users enter detailed search queries to improve accuracy, then search precision improves, but the ease of operation and user convenience deteriorates
Solution Approach 1:
The system dynamically adjusts the level of query refinement based on the initial search input and user preferences. For simple queries, the system performs minimal refinement to maintain speed and ease of use, while for ambiguous queries, it applies more extensive iterative refinement. This dynamic approach balances search precision with user convenience by adapting the refinement intensity to each specific search context
Data Source
AI summary
A computer-implemented system for interactive queries and searches is provided. The system may include a reference data database including reference data, a search term database including search terms, and a collaborator unit. The collaborator unit may receive a user input inputted into an interaction space of a user device and may compare and match the user input to the search terms to define a first query input. The collaborator unit may associate the first query input with portion(s) of the reference data to generate an output response. The collaborator unit may receive an additional user input and may compare and match the additional user input to the search terms to define a refined query input. The collaborator unit may associate the refined query input with portion(s) of the reference data and with the first output response to generate another output response.