Push Content Selection Using Historical Behavior Data
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Solution Overview
Problem
Existing methods for processing push content have lower accuracy and efficiency, particularly for objects with negative content interaction behaviors, as they fail to effectively utilize historical behavior data to select relevant content.
Innovation Solution
A content processing method and apparatus that determine a push content set based on a push content request, detect historical behavior feature data, and perform content-based matching or sorting to identify a target push content set for the object, enhancing content selection accuracy and efficiency by considering historical interaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to process push content, then the processing can be performed, but the push accuracy and efficiency are low for objects with negative content interaction behaviors
Solution Approach 1:
The system performs preliminary detection of historical behavior feature data before content matching. By pre-identifying objects with negative behavior patterns and retrieving their historical data in advance, the system avoids computationally intensive processing during the actual content matching phase, thereby improving both accuracy and efficiency.
Solution Approach 2:
The system utilizes the object's own historical behavior data to improve content matching accuracy. By leveraging self-generated historical interaction records, the system can more accurately predict content preferences without requiring extensive external data or complex processing, thus improving efficiency while maintaining high accuracy.
2Measurement precision
If historical behavior feature data is detected and used for content-based matching, then the push accuracy improves, but the processing complexity increases
Solution Approach 1:
The system extracts only the necessary historical behavior feature data relevant to content matching, rather than processing all available historical data. By selectively extracting pertinent features, the system maintains high push accuracy while reducing processing complexity and computational overhead.
Solution Approach 2:
The system applies different processing strategies based on the specific characteristics of each object's historical behavior data. For objects with well-defined negative behavior patterns, the system uses targeted content-based matching, while for others, it employs simpler sorting methods, thereby optimizing the balance between accuracy and complexity for each case.
3Measurement precision
If content-based matching is performed based on historical behavior feature data, then the relevance of selected content improves, but the time required for content selection increases
Solution Approach 1:
The system performs preliminary retrieval and filtering of historical behavior feature data before the actual content matching process. By preparing and organizing relevant historical data in advance, the system reduces the time required for content selection while maintaining high content relevance through accurate matching.
Solution Approach 2:
The system performs content-based matching only for objects with historical behavior data, while using simpler sorting methods for objects without such data. This partial application of the more time-consuming matching process maintains content relevance for those who benefit most from it while reducing overall processing time.
Data Source
AI summary
A method includes: obtaining a push content request from an object; determining a push content set according to the push content request; detecting historical behavior feature data of the object for a push content in response to a behavior feature of the object for the push content satisfying a preset negative behavior condition; performing content-based matching for the push content set based on the historical behavior feature data to obtain a target push content set of the object; performing content sorting of the push content set and performing content-based matching of the push content of the object to obtain a target push content set of the object; and determining a target push content of the object from the target push content set, and transmitting the target push content to the object.