Big data advertisement label classification system based on AI analysis

By generating a progressive resonance graph in a privacy sandbox environment and utilizing a hybrid attention parsing algorithm, the problems of label accuracy degradation and delivery imbalance in mobile advertising are solved, precise advertising delivery and resource optimization are achieved, and user experience and brand safety are improved.

CN120822133AActive Publication Date: 2025-10-21ZHEJIANG XINLAN NETWORK MEDIA LTD CO
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Patent Information

Application Number
CN202511302065.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-12
Publication Date
2025-10-21
Estimated Expiration
2045-09-12

AI Technical Summary

Technical Problem

In the privacy sandbox transformation environment, in mobile advertising delivery, the existing advertising label classification architecture that relies on artificial intelligence faces the problems of declining label accuracy and unbalanced delivery, resulting in waste of resources and poor advertising experience.

Method used

Through the topic summary and context identification of the delivery material on the receiving end, a progressive resonance graph is generated using the cross-window semantic resonance retrieval algorithm, the semantic drift curvature and path stability potential are calculated, and the hybrid attention parsing algorithm is combined to generate refined interest tags to ensure that the audience portrait closely follows real-time interests and synchronizes bidding decisions.

Benefits of technology

It achieves precise advertising delivery, reduces budget waste, improves advertising experience, reduces brand safety risks, and ensures real-time and accuracy of delivery.

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Abstract

The invention discloses a big data advertisement label classification system based on AI analysis, particularly relates to the field of advertisement data processing and label intelligent classification, and is used for solving the problems of interest identification distortion and released label drift caused by coarse granularity of an end-side theme abstract. Judging the combination credibility according to two parameters of a curvature amplitude index and a path stability potential so as to remodel a pseudo-continuous trajectory, and deducing a fine grain interest label under mixed attention modulation; according to the overall architecture, information faults after abstract compression are broken through, audience portraits are close to real-time interests and do not drift along with data reduction, a delivery end bids accurately according to the audience portraits, repeated correction around distortion labels is not needed any more, budget waste is greatly reduced, and exposure frequency distribution tends to be balanced; the progressive resonance graph and the label tree mapping library are kept synchronous, the real-time bidding engine always anchors the latest interest coordinate, the advertisement experience meets the user demand, and the brand security risk is inhibited along with the decline of the false bidding.
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