Keyword-Specific Expert Rating System for Query Matching
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Solution Overview
Problem
Conventional query and answer methods fail to accurately identify users with relevant knowledge for specific queries, leading to unsatisfied questioners even when expert point values are high, as they may not reflect the user's knowledge association with the query.
Innovation Solution
A method to compute a knowledge-based expert rating value by assigning weights to user interactions with keywords, including self-identification as an expert, information selection, question answering, and query/answer metrics, to rank users by their expertise for each keyword, enabling better matching of queries with knowledgeable answerers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user performs huge amounts of answer activities to maintain greater expert point values, then the user's overall expertise score increases, but it becomes impossible to identify whether the user has knowledge associated with a specific query keyword
Solution Approach 1:
The patent segments the overall expertise rating into keyword-specific ratings. Instead of a single aggregate expert point value, the system creates separate rating values for different keywords or topics. This allows precise identification of a user's knowledge in specific domains while maintaining the overall activity-based rating structure.
Solution Approach 2:
The patent applies local quality by making the expertise rating topic-dependent. Each keyword receives its own rating value that reflects the user's specific knowledge in that area. This enables the system to identify users with relevant knowledge for specific queries while preserving the global expert point accumulation mechanism.
2Ease of operation
If a questioner selects an answerer based only on expert point values, then the selection process is simple, but the questioner may be unsatisfied with the answer quality
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing keyword-specific expert ratings for all users before queries are submitted. When a query arrives with associated keywords, the system can immediately retrieve and match users with high ratings for those specific keywords, enabling both simple selection and high reliability without real-time computation.
Solution Approach 2:
The patent introduces keyword-specific expert ratings as an intermediary between the simple expert point system and the query matching process. This intermediary layer translates general expertise into topic-specific competence, allowing the system to maintain operational simplicity while improving answer quality through more accurate user-query matching.
3Measurement precision
If the system computes knowledge-based expert rating values for all users and keywords, then query-answer matching accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent computes keyword-specific expert ratings in advance during user answer activities, storing these pre-computed values in a database. When queries are submitted, the system retrieves pre-calculated ratings rather than performing complex computations in real-time, thus maintaining high matching accuracy while preserving system productivity.
Solution Approach 2:
The patent implements partial action by computing and storing only the necessary keyword-specific ratings that are actually needed for query matching. Rather than computing all possible combinations of user-keyword pairs, the system calculates ratings based on actual user activities and answers, reducing unnecessary computational overhead while maintaining accuracy for relevant matches.
Data Source
AI summary
A method and system for computing an expert point value of a user for each keyword is provided, including: a first step of assigning a first knowledge-based expert rating value associated with a keyword to a user identifier of a user which enters the keyword into an expert field; a second step of assigning a second knowledge-based expert rating value associated with the keyword to the user identifier when another user stores the keyword to correspond to the user identifier; a third step of assigning a third knowledge-based expert rating value associated with the keyword to the user identifier when the user of the user identifier performs an answer activity for the keyword; and a fourth step of computing the knowledge-based expert rating value of the user identifier for the keyword based on at least one of the first, the second, and the third knowledge-based expert rating values.