Semantic Search Engine Using Multi-Edge Directed Graph for Content Similarity
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Solution Overview
Problem
Current CRM systems lack effective methods to recommend content similar to user searches based on complex metadata, such as plot similarities, which are typically written in prose and difficult to analyze.
Innovation Solution
A search engine that utilizes a content storage manager to perform semantic synthesis of metadata, creating a multi-edged directed graph to calculate the strength of similarity between content items, including genre, cast, director, and plot, and provides recommendations based on this analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic synthesis of metadata is performed to analyze plot similarities, then content recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex metadata analysis by creating a multi-edged directed graph where different types of metadata (genre, cast, director, plot) are represented as separate edges between content nodes. This segmentation allows the system to handle complex semantic analysis through modular graph operations rather than monolithic processing.
Solution Approach 2:
The patent introduces a content storage manager as an intermediary component that performs semantic synthesis of metadata and manages the multi-edged directed graph. This intermediary handles the complexity of plot similarity analysis and metadata processing, shielding the search engine from direct complexity while maintaining high recommendation accuracy.
2Reliability
If multiple metadata types are analyzed for content similarity, then recommendation relevance is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing the multi-edged directed graph that encodes similarities across multiple metadata types. By preparing this graph structure in advance, the system avoids performing complex multi-metadata analysis during actual search operations, thus maintaining high recommendation relevance while reducing processing time for user queries.
3Measurement precision
If plot metadata is included in similarity analysis, then content matching accuracy is improved, but data processing difficulty increases
Solution Approach 1:
The patent applies parameter changes by transforming plot metadata from its original prose form into structured graph representations with weighted edges. The content storage manager synthesizes plot similarities by converting unstructured text into quantifiable parameters that can be efficiently processed and compared within the multi-edged directed graph framework.
Data Source
AI summary
A method storing information that indicates similarity between contents based on content metadata associated with each content, wherein a degree of similarity between contents is based on a semantic interpretation of plot metadata included in content metadata and pertaining to each plot associated with each content; receiving a search query from a user; retrieving one or more content metadata pertaining to one or more contents based on the search query; retrieving one or more recommended content metadata pertaining to one or more recommended contents that are similar to the one or more content metadata pertaining to the one or more contents based on the stored information; and providing a search result that includes the one or more content metadata and the one or more recommended content metadata via a user interface.