Apple Inc.

Daniel Joseph Sherman

Eswar Priyadarshan

Andrew Williams

Christopher Laurence Bell

David E Ralston

Jiachen Li

Zhou Li

This application relates to a systems and methods for trending issue identification in text streams. In one embodiment, a method for improving resolution of a trending issue identified in a set of text streams includes presenting a user interface of an application that is being executed by a computing device. The method also includes receiving a notification including the trending issue that has been identified in the set of text streams based at least in part on textual analysis performed on the set of text streams, and presenting the trending issue on the user interface of the application to enable an action to be performed to resolve the trending issue.

Methods and systems for trending issue identification in text streams

A media item recommendation can be based on application purchase data. Application purchase data of a population of users can be used to create application preference clusters that represent unique application preference profiles. Media preference data of the same population of users can be used to create media preference clusters that represent unique media preference profiles. A preference relationship between an application preference cluster and a media preference cluster can be identified based on the number of members of the media preference cluster that are also members of the application preference cluster. Upon receiving a recommendation request from a user, the user's application preference profile can be determined based on the user's application purchase data. A media item can then be recommended based on a media preference cluster that has a preference relationship with an application preference cluster that represents the application preference profile of the user.

Recommending media items based on purchase history

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San Francisco, CA, United States of America

Daniel Joseph Sherman