Abstract: In some aspects, the techniques described herein relate to a method including: receiving, by a computing device, a message corresponding to a user inbox and to be added to the user inbox; applying, by the computing device, prior to adding the message to the user inbox, a message classification model to content of the message to determine one or more classifications corresponding to the message; determining, by the computing device, that the message is an important message based on whether one or more of the classifications is one of a set of predetermined classifications; adding, by the computing device, metadata to the message, the added metadata indicating that the message is an important message; and transmitting, to the user inbox, by the computing device, the message and the added metadata.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, by a computing device, a message corresponding to a user inbox and to be added to the user inbox; applying, by the computing device, prior to adding the message to the user inbox, a message classification model to content of the message to determine one or more classifications corresponding to the message; determining, by the computing device, that the message is an important message based on whether one or more of the classifications is one of a set of predetermined classifications; adding, by the computing device, metadata to the message, the added metadata indicating that the message is an important message; and transmitting, to the user inbox, by the computing device, the message and the added metadata.

Abstract: The disclosed systems and methods provide a novel action prediction framework that performs personalized action prediction. According to an embodiment, the disclosed framework is able to dynamically predict which action (if any) a user might perform in response to receiving a given message. In some embodiments, for a given message, the action prediction framework can determine the probability that a user (e.g., sender, recipient) associated with the message may perform an action or set of action actions (e.g., open, forward, delete, reply, archive) related to the message. In some embodiments, the framework may be used to suggest a predicted action to the user. In some embodiments, a computing device may use the predicted actions to automatically perform the action. According to an embodiment, the action prediction framework includes a multi-label or multi-class model using a neural network.

Abstract: Users receive content recommendations from a personalized, generalized recommendation service that aggregates and selects content of high personal relevance to each individual user from a large pool of both personal and public content. The received content is filtered and the content determined to be relevant is cached. When a user request for content is received, the cached content is rescored and the content determined to be most relevant based on satisfaction of a relevance threshold is selected and forwarded to the user. Feedback methodologies are also implemented so that a user's actions are taken into consideration in real time and can affect subsequent recommendations to the user.

Abstract: Users receive content recommendations from a personalized, generalized recommendation service that aggregates and selects content of high personal relevance to each individual user from a large pool of both personal and public content. The received content is filtered and the content determined to be relevant is cached. When a user request for content is received, the cached content is rescored and the content determined to be most relevant based on satisfaction of a relevance threshold is selected and forwarded to the user. Feedback methodologies are also implemented so that a user's actions are taken into consideration in real time and can affect subsequent recommendations to the user.

Abstract: Users receive content recommendations from a personalized, generalized recommendation service that aggregates and selects content of high personal relevance to each individual user from a large pool of both personal and public content. The received content is filtered and the content determined to be relevant is cached. When a user request for content is received, the cached content is rescored and the content determined to be most relevant based on satisfaction of a relevance threshold is selected and forwarded to the user. Feedback methodologies are also implemented so that a user's actions are taken into consideration in real time and can affect subsequent recommendations to the user.

Abstract: Users receive content recommendations from a personalized, generalized recommendation service that aggregates and selects content of high personal relevance to each individual user from a large pool of both personal and public content. The received content is filtered and the content determined to be relevant is cached. When a user request for content is received, the cached content is rescored and the content determined to be most relevant based on satisfaction of a relevance threshold is selected and forwarded to the user. Feedback methodologies are also implemented so that a user's actions are taken into consideration in real time and can affect subsequent recommendations to the user.

Abstract: Users receive content recommendations from a personalized, generalized recommendation service that aggregates and selects content of high personal relevance to each individual user from a large pool of both personal and public content. The received content is filtered and the content determined to be relevant is cached. When a user request for content is received, the cached content is rescored and the content determined to be most relevant based on satisfaction of a relevance threshold is selected and forwarded to the user. Feedback methodologies are also implemented so that a user's actions are taken into consideration in real time and can affect subsequent recommendations to the user.

Abstract: Users receive content recommendations from a personalized, generalized recommendation service that aggregates and selects content of high personal relevance to each individual user from a large pool of both personal and public content. The received content is filtered and the content determined to be relevant is cached. When a user request for content is received, the cached content is rescored and the content determined to be most relevant based on satisfaction of a relevance threshold is selected and forwarded to the user. Feedback methodologies are also implemented so that a user's actions are taken into consideration in real time and can affect subsequent recommendations to the user.