Semantic Traffic Report Generation via Query Clustering
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Solution Overview
Problem
Existing search query reports in networked environments provide sparse performance data due to the large number of different search queries, limiting their usefulness in optimizing content campaigns.
Innovation Solution
A system and method that utilizes an entity engine and cluster engine to annotate and cluster search queries based on semantic concepts, generating aggregated performance statistics for improved content selection criteria, allowing for the addition of semantic positive or negative criteria to content campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search query reports are provided with detailed individual query data, then measurement precision is improved, but device complexity increases due to the large number of different search queries
Solution Approach 1:
The patent segments search queries into semantic clusters based on their meaning and intent. Instead of treating each query individually, queries are grouped into clusters that share common semantic characteristics. This segmentation approach reduces the number of distinct entities to manage while preserving the performance data accuracy of individual queries within each cluster.
Solution Approach 2:
The patent merges semantically similar search queries into unified clusters. By combining queries that have similar meanings and user intents into single semantic units, the system reduces the overall complexity of the report structure while maintaining the ability to analyze performance metrics with the precision needed for effective content optimization.
2Measurement precision
If individual search query performance metrics are tracked, then measurement precision is improved, but loss of information increases due to the overwhelming number of queries
Solution Approach 1:
The patent segments the vast number of search queries into manageable semantic clusters. Each cluster represents a coherent semantic unit containing multiple related queries. This segmentation allows the system to track individual query performance metrics with high precision while organizing the data in a way that reduces information overload and improves manageability.
Solution Approach 2:
The patent introduces semantic clusters as intermediary structures between individual queries and the final performance analysis. These clusters act as mediators that aggregate query data while preserving the essential performance information. The clusters organize and structure the data, making it more manageable while maintaining the precision needed for effective content campaign optimization.
3Ease of operation
If semantic clustering is applied to organize queries, then ease of operation is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent implements self-service through automated semantic clustering and performance aggregation. The system automatically identifies semantic relationships between queries, groups them into clusters, and calculates performance metrics without requiring manual intervention. This automation improves the ease of operation for content optimization while the processing complexity is managed through efficient algorithmic approaches.
Solution Approach 2:
The patent changes the parameter of query organization from individual query-level analysis to cluster-level analysis. By transforming the data structure from discrete queries to semantic clusters, the system simplifies the operations needed for content optimization. The clustering process reorganizes the data in a way that reduces the complexity of subsequent analysis and decision-making processes.
Data Source
AI summary
Systems and methods of this disclosure are directed to optimizing a content selector executing on content selection infrastructure. An entity engine retrieves a search query report with queries corresponding to selected content items of a content campaign and a performance metric for each query. The entity engine determines an entity for each of the queries. A cluster engine generates a first subset of the queries and a second subset of the queries based on the entity for each query. The cluster engine generates a first performance metric for the first subset and a second performance metric for the second subset. An interface displays the first performance metric and the second performance metric. A campaign generator receives a selection of a semantic criterion based on the first subset and updates the content campaign to include the semantic criterion.