Tutor Matching System Using Historical Search Term Mapping
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Solution Overview
Problem
Prior art tutor search systems rely heavily on manipulated tutor profiles and insufficient subject granularity, making it difficult to match students with the most suitable tutors for specialized subjects.
Innovation Solution
A computer-implemented method that utilizes a table of historical search terms to relate search queries to pertinent subjects, computes relevance scores based on tutor profiles, and presents a ranked list of tutors, adjusting scores for related terms and user reviews to ensure accurate matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If tutor profiles are used for matching, then the matching process is simple, but the profiles are susceptible to manipulation and lack sufficient subject granularity
Solution Approach 1:
The patent introduces an intermediary mapping system that connects search terms to subjects through a table of historical search terms and corresponding subjects. This intermediary layer prevents direct manipulation of tutor profiles while maintaining matching functionality, as the system now relies on the mapping table rather than directly parsing tutor profiles for subject classification.
Solution Approach 2:
The patent replaces the mechanical system of profile-based matching with a data-driven approach using historical search term data. Instead of relying on tutors to accurately self-classify their expertise in profiles, the system uses empirically derived mappings from actual student search behavior to determine subject relevance, substituting manual profile completion with automated historical data analysis.
2Ease of operation
If tutors are divided into common subjects, then the search system is easy to operate, but specialized subject areas cannot be adequately matched
Solution Approach 1:
The patent implements a dynamic subject mapping system where the relationship between search terms and subjects is not fixed but derived from historical data. The mapping table can adapt to new specialized subjects as they appear in student searches, allowing the system to maintain ease of operation with common subjects while simultaneously accommodating specialized subject areas through flexible, data-driven term-to-subject mappings.
Solution Approach 2:
The patent adds a new dimension to the subject classification system by introducing a mapping table that bridges search terms and subjects. This additional layer allows the system to handle both common subjects (through existing subject categories) and specialized subjects (through specific search term mappings), effectively adding granularity without complicating the user interface or search operation.
3Speed
If relevance scores are computed based on tutor profiles, then the matching is fast, but specialized search terms are not properly matched to tutors
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing a mapping table of historical search terms to subjects before the actual matching process. This allows the system to quickly lookup subject mappings during student-tutor matching without performing complex real-time analysis, maintaining fast matching speed while improving precision through pre-analyzed historical data that captures specialized subject relationships.
Data Source
AI summary
A system and method for matching students to tutors is disclosed. In particular, a user submits a query and in response a plurality of tutors are identified responsive to the search query. The identified tutors are then ranked based on relevance to the search query. The identified tutors are then presented to the user in an order based on the relevance score assigned to each tutor.