Quasi-Low Entropy Browsing History for Personalization

摘要

The present disclosure provides systems and methods for content quasi-personalized or anonymized content retrieval via the aggregated browsing history of multiple devices, such as millions or billions of devices. A sparse matrix can be constructed from aggregated browsing history and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via pseudo-personalized clusters that exhibit similar browsing history without exposing individual device details to content providers.