Content Search Ranking Using Popularity and Query Relevance
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Solution Overview
Problem
Standard search techniques struggle to differentiate between content items with short descriptors, leading to inadequate search results.
Innovation Solution
A system that determines a final relevance score for content items based on how well the search term matches the content, its popularity, and its relevance to the user, using heuristics and past search data to order and prioritize search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard search techniques are used to evaluate content items based on search term frequency, then the search process is simple and fast, but the search results lack differentiation and meaningful results when content descriptors are short
Solution Approach 1:
The patent transforms the search evaluation from a single parameter (search term frequency) to multiple parameters including popularity metrics, recency factors, and user interaction data. This multi-parameter approach enables meaningful differentiation between content items with short descriptors while maintaining systematic evaluation through weighted scoring mechanisms.
Solution Approach 2:
The search evaluation process is divided into distinct components: initial relevance scoring based on search terms, popularity scoring based on consumption data, and recency adjustments. Each component operates independently and contributes to the final ranking, allowing complex differentiation without overwhelming system complexity.
2Reliability
If content items are ranked solely by search term match frequency, then the ranking process is straightforward, but popular and currently relevant content may not be prioritized
Solution Approach 1:
The system incorporates feedback loops where consumption data, user interactions, and popularity metrics continuously inform the relevance scoring. Content items that are currently popular or frequently consumed receive boosted scores, ensuring that relevant and trending content is prioritized while maintaining the foundational search term matching mechanism.
Solution Approach 2:
The system pre-calculates popularity scores and recency factors for content items before search queries are executed. This preliminary preparation of auxiliary scoring data enables the integration of multiple relevance factors without significantly increasing real-time search complexity, as much of the heavy lifting is done in advance.
Data Source
AI summary
Methods and systems for performing a search are disclosed. An example method can comprise a server receiving a search query, wherein the search query comprises one or more keywords. The server can determine a first score indicating importance of a search term to one or more content items associated with a corpus. The server can receive past searches, each comprising a search term and a selected result, and determine a second score indicating importance of a search term to one or more of the content items based on the past searches. The server can determine a third score indicating an importance of each content item to the one or more keywords based on the first score and the second score. One or more of the plurality of content items can be sent to a user based on the third score.