Click rate prediction method and device, computer device and storage medium

By acquiring historical click-through rate (CTR) band charts and performing entity recognition and sentiment analysis on dynamic information, the problem of poor advertising performance was solved, the accuracy of CTR prediction was improved, and advertising strategies were optimized, thereby increasing advertising revenue.

CN113822066BActive Publication Date: 2026-06-16DONSON TIMES INFORMATION TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
DONSON TIMES INFORMATION TECH CO LTD
Filing Date
2021-08-17
Publication Date
2026-06-16

AI Technical Summary

⚠Technical Problem

Existing technologies often result in poor advertising performance and low returns, making it difficult to accurately assess the effectiveness of media platform campaigns and leading to inefficient advertising delivery.

⚗Method used

By acquiring historical click-through rate (CTR) band maps and target industry tags, dynamic news information within a preset time range is crawled, entity recognition and intent feature extraction are performed, it is determined whether the news industry tags are the same as the target industry tags, and when they are the same industry, industry sentiment analysis is performed. Finally, based on the sentiment analysis results, the CTR is predicted, and a predicted CTR band map is generated.

🎯Benefits of technology

It improves the accuracy of click-through rate prediction, enabling rapid adjustments to ad placement timing or strategies, thereby increasing the effectiveness and accuracy of ad placement.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a click rate prediction method and device, computer equipment and a storage medium, the method uses a crawler technology to crawl dynamic information in a preset time range; the preset time range is determined according to the historical statistical time; entity recognition and intent feature extraction are performed on the dynamic information to obtain entity recognition results and intent extraction results; the information industry label is determined according to the entity recognition results, and whether the information industry label and the target industry label are the same is determined; when the information industry label and the target industry label are the same, industry sentiment analysis is performed on the entity recognition results and the intent extraction results to obtain sentiment analysis results; the historical click rate wave band diagram is predicted according to the sentiment analysis results to obtain a predicted click rate wave band diagram. The application improves the accuracy of click rate prediction, and improves the feasibility and accuracy of the advertisement placement scheme recommendation.
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