FAQ Evaluation System Using User View History Analysis
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Solution Overview
Problem
Existing support systems face challenges in effectively managing and evaluating frequently asked questions (FAQs) to help users resolve issues independently, as less useful answer information may not be recognized, leading to inefficient use of staff resources and difficulty in properly managing view information.
Innovation Solution
An information management apparatus that stores view histories of user inquiries and FAQs, allowing for the extraction and evaluation of FAQ effectiveness by determining correlations between user issues and previously viewed FAQs, updating evaluation values based on user interactions, and providing a system for users to access FAQs and issue inquiries without staff intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FAQ evaluation is based on staff skill levels, then answer information quality is improved, but staff resources are consumed and manual evaluation is required
Solution Approach 1:
The system enables self-service evaluation by automatically analyzing user behavior data (view histories, inquiry contents, problem resolutions) to compute FAQ evaluation values without requiring staff intervention. This transforms the manual evaluation process into an automated self-evaluating system that continuously improves FAQ quality based on actual user interactions.
Solution Approach 2:
The system implements feedback mechanisms by collecting user interaction data with FAQs and using this information to automatically update evaluation values. The feedback loop continuously monitors whether users successfully resolve issues using FAQs, and adjusts evaluation values accordingly, enabling dynamic optimization of FAQ quality.
2Ease of operation
If more FAQs are provided to users, then user self-resolution capability is improved, but less useful answer information cannot be recognized and may clutter the system
Solution Approach 1:
The system changes the evaluation parameter from static staff-based skill levels to dynamic user-behavior-based metrics. By continuously updating evaluation values based on actual user interaction data (view histories, inquiry contents, resolution outcomes), the system automatically identifies and prioritizes useful FAQs while deprioritizing less effective ones, enabling users to easily find relevant information.
3Measurement precision
If manual evaluation of FAQs is performed, then evaluation precision is maintained, but processing load on support staff increases
Solution Approach 1:
The system replaces the mechanical manual evaluation process with an automated information processing system that analyzes user behavior data. Instead of staff manually reviewing and evaluating FAQs, the system automatically processes view histories, inquiry contents, and resolution data to compute evaluation values, substituting human mechanical evaluation with automated computational analysis.
Data Source
AI summary
An information management apparatus includes a memory configured to store view histories of information relating to inquiry information including identifiers of users and contents of inquiries, and a processor coupled to the memory and the processor configured to perform extraction of a first view history of a first user from the view histories in response to receiving first inquiry information including an identifier of the first user and a content of a first inquiry, perform, based on the content of the first inquiry, determination of whether the first view history of the first user includes first information relating to the first inquiry information, and perform modification of a first evaluation value associated with the first information when it is determined that the first view history includes the first information.