User-Topic Mapping Engine for Forum Expertise Matching
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Solution Overview
Problem
Existing online forum systems lack an efficient method to match conversations with relevant users who possess expertise on the topic, leading to suboptimal engagement and response quality.
Innovation Solution
A system comprising a topic engine, mapping engine, scoring engine, and matching engine that maps documents to topics, assigns expertise scores, and matches users to conversations based on these mappings, utilizing algorithms like PARIS to discern topics and calculate user relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually browse forum conversations to find relevant experts, then user engagement may occur, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by pre-mapping users to topics based on their document contributions before matching is needed. The mapping engine continuously builds and updates user-topic mappings as users add documents, so when a conversation needs matching, the relevant users are already identified and ready, eliminating the need for real-time analysis
Solution Approach 2:
The patent introduces topic mappings as an intermediary layer between users and conversations. Instead of directly matching users to conversations, the system uses topic mappings derived from user documents as a mediator. This intermediary structure enables efficient matching by translating both user expertise and conversation topics into a common topic-based language
2Measurement precision
If the system maps each user to multiple topics based on document associations, then matching accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of user-expertise identification into distinct functional components: a mapping engine that creates user-topic mappings based on documents, a scoring engine that calculates expertise scores, and a matching engine that performs the actual matching. This segmentation allows each component to specialize in one aspect, improving overall accuracy while managing complexity through modular design
Solution Approach 2:
The topic mapping structure serves multiple functions simultaneously: it represents user expertise, enables conversation matching, provides a basis for scoring, and facilitates scalability. By creating a universal topic-based framework, the system avoids needing separate mechanisms for each function, reducing overall system complexity while maintaining high measurement precision
Data Source
AI summary
A method for matching a user to a conversation includes, for each of a plurality of documents, mapping the document to one or more of a plurality of topics. The document is associated with a user. For each user, the user is mapped to each of the plurality of topics that are mapped to documents with which the user is associated. One of the users is matched to a conversation according to the mappings of the users to those of the plurality of topics that are also mapped to the conversation.