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 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. 本公开提供了用于经由诸如数百万或数十亿个设备之类的多个设备的聚合的浏览历史来进行内容准个性化或匿名化内容检索的系统和方法。稀疏矩阵可以从聚合的浏览历史中构建，并且在维度上减小，从而减小熵并且为各个设备提供匿名性。可以经由用于表示相似浏览历史的准个性化聚类来选择相关内容，而无需将各个设备细节暴露于内容提供者。