Search Result Clustering via User Content Features
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Solution Overview
Problem
Search engines often return redundant, irrelevant, and unorganized results to user queries, making it difficult for users to find high-quality and relevant content, such as vacation information or images, amidst thousands of unorganized results.
Innovation Solution
Organizing search results using user-generated content by clustering similar features, such as images, videos, and text, based on metadata analysis and machine learning techniques like the multimodal Dirichlet Process Mixture Sets model, to rank and present relevant content more effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines return comprehensive results to user queries, then the quantity of information provided increases, but the organization and relevance of results deteriorate
Solution Approach 1:
The patent segments search results into multiple clusters based on extracted features from user-generated content. Each cluster represents a distinct category or topic, allowing users to navigate organized groups of results rather than a flat, unorganized list. This segmentation resolves the contradiction by maintaining comprehensive results while improving findability through structured organization.
Solution Approach 2:
The system changes the organizational parameters of search results by extracting features from user-generated content and using these features to cluster results. Instead of traditional keyword-based sorting, results are reorganized based on semantic features and relationships derived from user content, transforming the parameter structure to enhance relevance and organization.
2Device complexity
If search engines provide unorganized results, then the complexity of the search system is reduced, but the quality and relevance of information retrieval deteriorates
Solution Approach 1:
The system performs preliminary actions by extracting features from user-generated content before organizing search results. This pre-processing step creates a feature database that guides the clustering of results, ensuring relevance is established before the user encounters the results. This preliminary organization maintains reliability while keeping the actual search interface relatively simple.
3Measurement precision
If users manually sift through search results, then the precision of finding desired content may improve, but the time required for search increases
Solution Approach 1:
The patent replaces the manual mechanical process of sifting through results with an automated computational system that extracts features from user-generated content and automatically clusters results. This substitution maintains precision by using semantic analysis while eliminating the time loss associated with manual browsing, as the system pre-organizes results based on extracted features.
Data Source
AI summary
Many users make use of search engines to locate desired internet content by submitting search queries. For example, a user may search for photos, applications, websites, videos, documents, and/or information regarding people, places, and things. Unfortunately, search engines may provide a plethora of information that a user may be left to sift through to find relevant content. Accordingly, one or more systems and/or techniques for organizing search results are disclosed herein. In particular, user generated content, such as photos, may be retrieved based upon a search query. The user generated content may be grouped into clusters of user generated content having similar features. Search results of the search query may be obtained and organized based upon comparing the search results with the clusters. The organized search results and/or a table of content including the clusters may be presented to provide an enhanced user experience.