LOW ENTROPY BROWSING HISTORY FOR CONTENT QUASI-PERSONALIZATION

LOW ENTROPY BROWSING HISTORY FOR CONTENT QUASI-PERSONALIZATION

The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: YUNG, Marcel M. M, WANG, Gang
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Beschreibung
Zusammenfassung:The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.