ML Ranking Module for Technical Solution Retrieval
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Solution Overview
Problem
Users face inefficiencies in finding effective solutions to technical computer problems as they often need to sift through numerous search results, with some solutions being unsuitable due to hardware or software limitations, and existing methods lack personalized ranking based on user-specific criteria.
Innovation Solution
A machine learning module is employed to rank technical solutions by considering user profile information, technical specifications, and specific criteria, optimizing the selection of answer files by excluding irrelevant options and providing the most relevant solutions based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users review numerous search results to find solutions, then they may eventually find a working solution, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-ranking solutions using machine learning models before users review them. The ML module analyzes user profiles, system specifications, and solution compatibility in advance, presenting the most relevant solutions first. This eliminates the need for users to manually review numerous unranked results, significantly reducing time while maintaining solution effectiveness.
2Adaptability or versatility
If all available solutions are provided to users, then comprehensive coverage is achieved, but users face difficulty selecting the most suitable solution for their specific hardware and software configuration
Solution Approach 1:
The system applies local quality by customizing the solution presentation based on each user's specific context. The ML module analyzes user profiles, system specifications (hardware, software, OS version), and compatibility requirements to tailor the ranked list. Each user receives a personalized subset of solutions optimized for their specific configuration, making selection easy while maintaining comprehensive coverage of all possible solutions in the database.
Solution Approach 2:
The system replaces the manual mechanical process of user filtering and selection with an automated ML-based ranking system. The ML module automatically matches user specifications with solution compatibility, performing the selection task that would otherwise require complex user analysis. This substitution maintains comprehensive solution coverage while dramatically improving ease of selection.
3Device complexity
If generic search results are returned without personalization, then system complexity is reduced, but solution relevance to specific user configurations decreases
Solution Approach 1:
The system changes parameters by incorporating multiple input variables (user profile attributes, system specifications, solution compatibility metrics) into the ML ranking model. These parameter changes enable precise relevance measurement while managing complexity through the use of trained ML models that automatically process the inputs. The ML module transforms multiple parameters into a single ranked output, maintaining manageable system complexity while achieving high solution relevance.
Data Source
AI summary
Provided are a computer program product, system, and method for using a machine learning module to rank technical solutions to user described technical problems to provide to a user. A solutions database is searched to determine a result set of answer files having keywords related to keywords of a technical problem in a user description. The result set of answer files, the user description of the technical problem, user profile information, and technical specifications of the computing system are provided as input to a machine learning module. The machine learning module outputs ranks of the answer files in the result set. A subset of answer files in the result set is selected based on ranks of the answer files in the result set and returned to the user.