Search Method Using Concept Maps for Group Recommendation
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Solution Overview
Problem
Existing search methods struggle to recommend products or services to groups without prior purchase or usage histories, as they rely on input scores or buying behavior, which are not applicable to all product fields and fail to account for shared user concepts.
Innovation Solution
A search method that utilizes concept maps unique to individual users within a group, generating association maps to extract associated words based on semantic distances, allowing for the recommendation of products or services without purchase histories by identifying shared concepts among group members.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If purchase histories or input scores are used as the basis for recommendation, then recommendation accuracy for items with existing data is improved, but the system cannot recommend items without prior purchase or usage histories
Solution Approach 1:
The patent introduces concept maps as an intermediary between users and items. Instead of directly using purchase histories or scores, the system maps user concepts (semantic structures representing user preferences and associations) to bridge the gap for items without historical data. This mediator enables recommendations for new items by comparing conceptual relationships rather than relying on existing transaction records.
Solution Approach 2:
The patent replaces the mechanical system of direct score comparison and purchase history matching with a semantic-based conceptual framework. By substituting the traditional recommendation mechanism (based on explicit user inputs and transaction records) with a system that analyzes semantic distances and conceptual associations in concept maps, the system can infer preferences for items without prior interaction data.
2Adaptability or versatility
If individual user concept maps are created to capture personal preferences, then recommendation personalization is improved, but the system cannot identify shared concepts among group members
Solution Approach 1:
The patent merges individual user concept maps to create a group concept map that preserves both personal preferences and shared concepts. By combining multiple individual concept maps while maintaining their unique semantic structures, the system identifies overlapping concepts that represent group consensus. This merging process allows the system to simultaneously capture individual user characteristics and common group preferences for recommendation purposes.
Data Source
AI summary
A method includes (a) obtaining a search word, (b) obtaining first to third concept maps including words and semantic distances between the words, (c) obtaining a first association map including degrees of association indicating how close the semantic distances included in the first and second concept maps are to each other; (d) obtaining a second association map including degrees of association indicating how close the semantic distances included in the first to third concept maps are to one another, (e) extracting, from the words as an associated word, at least one word whose difference between the degree of association with the search word included in the first association map and the degree of association with the search word included in the second association map is equal to or larger than a first threshold, and (f) outputting a result of a search based on the search word and the associated word.