Paypal, Inc.

Rajesh Bala Kancharla

Yuan Deng

Techniques are disclosed relating to generating user-specific time-to-live (TTL) values using machine learning. In various embodiments, a server system maintains a cache data store that is operable to store data for a plurality of users of a web service. In response to a cache miss for a request from a first one of the plurality of users, the server system may generate a user-specific TTL value for the first user. In various embodiments, generating the user-specific TTL value may include using a machine learning model to generate a predicted future access pattern for the first user that indicates a distribution of time periods during which the first user is expected to access the web service and, based on the predicted future access pattern, determining the user-specific TTL value for the first user.

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Singapore, Singapore

Rajesh Bala Kancharla