Content Gap Detection via Search Topic Analysis
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Solution Overview
Problem
Content delivery platforms often face issues where searches yield few or no relevant results, limiting their usefulness and user engagement, as they lack content items that users frequently seek.
Innovation Solution
A computer-implemented method that retrieves historical search data to identify semantically similar search queries, maps these queries to content item metadata, and determines a content score to detect content gaps, generating alerts for underrepresented search topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the breadth of content is increased to improve search result coverage, then more search queries can return useful results, but content that is rarely or never sought out adds little or no value
Solution Approach 1:
The system performs preliminary analysis of historical search data to identify content gaps before content is created. By analyzing search queries and determining content scores in advance, the system proactively identifies what content is needed, ensuring that content creation efforts are directed toward topics that will actually serve user needs rather than simply filling volume requirements
2Productivity
If historical search data is analyzed to identify content gaps, then content creation can be prioritized effectively, but the complexity of the system increases
Solution Approach 1:
The system segments the content gap identification process into distinct modular components: historical search data retrieval, search topic determination through semantic clustering, search topic descriptor generation, content score determination, and content gap identification. Each module performs a specific function and can be independently implemented or modified, reducing overall system complexity while maintaining analytical capability
Solution Approach 2:
The system introduces search topic descriptors as intermediary elements between raw search queries and content analysis. These descriptors serve as a bridge that simplifies the connection between user search behavior and content inventory assessment, making the system more manageable by abstracting complex search pattern analysis into standardized topic representations
Data Source
AI summary
Described herein is a computer implemented method including: retrieving historical search data including a plurality of historical search queries corresponding to historical searches for content items provided by a content delivery platform; processing the historical search data to determine a plurality of search topics, each search topic corresponding to a group of semantically similar historical search queries. The method further includes performing a first search for content items provided by the content delivery platform that are relevant to the first search topic and determining, based on results of the first search, a first content score that provides a measure of how much content provided by the content delivery platform is relevant to the first search topic and determining, based on the first content score, whether a content gap exists for the first search topic. In response determining that the content gap exists, a first content gap alert is generated.