Topic Model Search Refinement for Social Media
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Solution Overview
Problem
Users face challenges in refining search queries for user-generated content on social media platforms due to ambiguous search results, requiring multiple iterations to exclude irrelevant content and ensure relevance, which can be time-consuming and requires expert knowledge.
Innovation Solution
A computer-implemented method that applies a topic model to sample documents received from a content provider, generates a topic representation, and allows users to assign relevance scores, enabling the classification of data sources and the determination of a refined search request with increased selectivity on high-scoring topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If keyword-based retrieval is used for searching user-generated content, then the search can be performed quickly, but the search results become ambiguous and require multiple refinement iterations
Solution Approach 1:
The system performs preliminary topic modeling on sample documents before the user needs to refine their search. By pre-computing topic representations and assigning them to documents, the system prepares relevance information in advance, enabling faster and more accurate search refinements without requiring multiple manual iterations.
Solution Approach 2:
Topic models serve as an intermediary between the user's initial keyword search and the final relevant documents. The topic model translates ambiguous keywords into structured topic representations, which then mediate the retrieval process by enabling precise filtering and ranking of documents based on topic relevance rather than direct keyword matching.
2Measurement precision
If manual refinement of search queries is performed to eliminate irrelevant content, then search result quality improves, but the process becomes time-consuming and requires expert knowledge
Solution Approach 1:
The system performs self-service by automatically generating topic representations and relevance scores without requiring user expertise. The topic model autonomously analyzes sample documents, identifies relevant topics, and ranks them, eliminating the need for users to manually refine queries while maintaining high search result quality.
Solution Approach 2:
The system provides feedback by presenting topic representations and relevance scores to users, allowing them to quickly identify and select relevant topics without manual query refinement. This feedback mechanism enables users to directly access organized relevance information and make informed selection decisions, significantly reducing refinement time.
3Measurement precision
If sample documents are retrieved and analyzed to determine topic relevance, then refined search results can be obtained, but additional processing time and computational resources are required
Solution Approach 1:
The system performs preliminary topic modeling on sample documents before final search refinement is needed. By pre-computing topic representations and storing them for later retrieval, the system reduces the computational burden during actual search operations, as the complex analysis has already been performed in advance.
Solution Approach 2:
The system applies partial action by analyzing only the necessary portion of documents (sample documents) rather than processing entire datasets. This selective processing approach maintains sufficient topic relevance classification accuracy while significantly reducing computational resources and processing time requirements.
Data Source
AI summary
A computer-implemented method, a computer program product, and a computer system for refining an initial search request to a content provider. The computer-implemented method includes receiving the initial search request from a user, submitting the initial search request to the content provider, receiving from the content provider a set of sample documents and source identifiers for respective ones of the sample documents, applying a topic model to the set of the sample documents to obtain a topic representation, presenting the topic representation to the user, receiving from the user topic relevance scores for respective ones of the topics, and classifying the data sources according to the topic relevance scores to obtain source relevance scores for respective ones of the data sources. The computer-implemented method further includes, based on the source relevance scores, determining a refined search request having an increased selectivity on documents covering a highest-scoring one of the topics.