Abstract: The present disclosure provides systems and methods for client-side anonymized content selections. The method includes collecting a plurality of identifications of content selection lists, each content selection list associated with the first device and at least one other device. The method includes selecting a first subset of the content selection lists, responsive to a total number of unique device associated with a plurality of content selection lists of the first subset exceeding a threshold. The method includes transmitting a request for an item of content, the request comprising identifications of the content selection lists of the selected first subset. The method includes receiving a first item of content selected by the content server based on the content selection lists of the selected first subset.

Abstract: 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.

Abstract: The present disclosure provides systems and methods for client-side anonymized content selections. The method includes collecting a plurality of identifications of content selection lists, each content selection list associated with the first device and at least one other device. The method includes selecting a first subset of the content selection lists, responsive to a total number of unique device associated with a plurality of content selection lists of the first subset exceeding a threshold. The method includes transmitting a request for an item of content, the request comprising identifications of the content selection lists of the selected first subset. The method includes receiving a first item of content selected by the content server based on the content selection lists of the selected first subset.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure provides systems and methods for client-side anonymized content selections. The method includes collecting a plurality of identifications of content selection lists, each content selection list associated with the first device and at least one other device. The method includes selecting a first subset of the content selection lists, responsive to a total number of unique device associated with a plurality of content selection lists of the first subset exceeding a threshold. The method includes transmitting a request for an item of content, the request comprising identifications of the content selection lists of the selected first subset. The method includes receiving a first item of content selected by the content server based on the content selection lists of the selected first subset.

Abstract: The present disclosure provides systems and methods for client-side anonymized content selections. The method includes collecting a plurality of identifications of content selection lists, each content selection list associated with the first device and at least one other device. The method includes selecting a first subset of the content selection lists, responsive to a total number of unique device associated with a plurality of content selection lists of the first subset exceeding a threshold. The method includes transmitting a request for an item of content, the request comprising identifications of the content selection lists of the selected first subset. The method includes receiving a first item of content selected by the content server based on the content selection lists of the selected first subset.

Abstract: 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.

Abstract: The present disclosure provides systems and methods for client-side anonymized content selections. The method includes collecting a plurality of identifications of content selection lists, each content selection list associated with the first device and at least one other device. The method includes selecting a first subset of the content selection lists, responsive to a total number of unique device associated with a plurality of content selection lists of the first subset exceeding a threshold. The method includes transmitting a request for an item of content, the request comprising identifications of the content selection lists of the selected first subset. The method includes receiving a first item of content selected by the content server based on the content selection lists of the selected first subset.