Abstract: Various methods for combining ClipRank and Collaborative Filtering are provided. According to one embodiment, the ClipRank weights associated with pieces of media content are calculated based on the relationships among the pieces of media content and various users. Those pieces having ClipRank weights greater than or equal to a predefined weight threshold are selected from the pieces of media content to obtain selected pieces of media content. Collaborative Filtering is then performed on the selected pieces of media content and the users.

Abstract: System and method for automatically rating the content of video media based on video operations performed on a media device and in reference to a plurality of rating rules are provided. Usage of the media device is continuously monitored and user actions with respect to operating the video media on the media device are automatically logged. Each rating rule includes a device usage pattern with respect to operating videos on the media device and a rating action indicating adjustments to content ratings of the videos based upon characteristics described by the device usage pattern that are inferred from the recorded user inputted video control operations. When the device usage pattern of a rating rule is inferred from one or more user actions operating a piece of video media directly on the media device, the content rating of the piece of video media is adjusted based on the rating rule.

Abstract: System and method for performing Collaborative Filtering while preserving complete user anonymity are provided. Each of a group of client devices sends a rating vector anonymously to a server. The cells in each rating vector correspond to a set of items, and selected cells have ratings provided by the user associated with the corresponding client device for the corresponding items. The server aggregates all the rating vectors into a rating matrix, and factorizes the rating matrix into a user feature matrix and an item feature matrix through approximation, such that the rating matrix equals the product of the user feature matrix and the item feature matrix. The item feature matrix is sent to the client devices. Each of the client devices calculates its own user feature vector based on its rating vector and the item feature matrix, and provides personalized recommendations on selected items based on the client's user feature vector and the item feature matrix.

Abstract: System and method for performing Collaborative Filtering while preserving complete user anonymity are provided. Each of a group of client devices sends a rating vector anonymously to a server. The cells in each rating vector correspond to a set of items, and selected cells have ratings provided by the user associated with the corresponding client device for the corresponding items. The server aggregates all the rating vectors into a rating matrix, and factorizes the rating matrix into a user feature matrix and an item feature matrix through approximation, such that the rating matrix equals the product of the user feature matrix and the item feature matrix. The item feature matrix is sent to the client devices. Each of the client devices calculates its own user feature vector based on its rating vector and the item feature matrix, and provides personalized recommendations on selected items based on the client's user feature vector and the item feature matrix.

Abstract: Various methods for combining ClipRank and Collaborative Filtering are provided. According to one embodiment, the ClipRank weights associated with a plurality of pieces of media content are calculated based on the relationships among the plurality of pieces of media content and a plurality of users. Those pieces having ClipRank weights greater than or equal to a predefined weight threshold are selected from the plurality of pieces of media content to obtain a plurality of selected pieces of media content. Collaborative Filtering is then performed on the plurality of selected pieces of media content and the plurality of users. According to another embodiment, Collaborative Filtering on a plurality of pieces of media content and a plurality of users is performed for one of the plurality of users.

Abstract: System and method for automatically rating the content of video media based on video operations performed on a media device and in reference to a plurality of rating rules are provided. Usage of the media device is continuously monitored and user actions with respect to operating the video media on the media device are automatically logged. Each rating rule includes a device usage pattern with respect to operating videos on the media device and a rating action indicating adjustments to content ratings of the videos based upon characteristics described by the device usage pattern that are inferred from the recorded user inputted video control operations. When the device usage pattern of a rating rule is inferred from one or more user actions operating a piece of video media directly on the media device, the content rating of the piece of video media is adjusted based on the rating rule.