Abstract: A system and method to navigate through a media item catalog and generate recommendations using behavioral metrics such as correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). One implementation uses a knowledge base comprising a collection of mediasets. Various metrics (Metric 1-Metric m) among media items (m1 . . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).

Abstract: A system and method to navigate through a media item catalog and generate recommendations using behavioral metrics such as correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). One implementation uses a knowledge base comprising a collection of mediasets. Various metrics (Metric 1-Metric m) among media items (m1 . . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).

Abstract: A system and method to navigate through a media item catalog using correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). The system uses a knowledge base consisting of a collection of mediasets. Various metrics (Metric 1-Metric m) among media items (m1. . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: A system to navigate through a media item catalog using correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). The system uses a knowledge base consisting of a collection of mediasets. Various metrics (Metric 1-Metric m) among media items (m1 . . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).

Abstract: Systems and methods are disclosed for identifying a new set of media items in response to an input set (or “query set”) of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or “similarity” metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of media items.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Systems and methods are disclosed for identifying a new set of media items in response to an input set (or “query set”) of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or “similarity” metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of media items.

Abstract: The present describes systems and methods to support personal financial management by recommending financial products and services responsive to a user's particular financial health or situation based on explicit and implicit user data and feedback.

Abstract: Disclosed are various embodiments of systems and methods for generating composite mediasets from mediasets, each comprising media items, associated with a plurality of users. In some embodiments, individual and/or group recommendations are provided for creating a group playlist by aggregating user taste data for a plurality of users in a group. In other embodiments, systems and methods are provided which allow for sharing and playing of a group playlist by users in a group, each of which has a media playback device. In still other embodiments, systems and methods are provided for dynamically building a composite playlist that may be updated as users enter and/or leave the group. One example of an interactive entertainment application also is described for use at a venue to entertain live guests with music selections that are dynamically influenced by the guests' individual tastes.

Abstract: Disclosed are embodiments of systems and methods for prioritizing mobile media player files by providing for the automated addition and/or deletion of media items for a mobile media player. In some embodiments, a statistical method may be provided for inferring which media items on a mobile media player should be deleted based on, for example, user taste data. In some embodiments, new media items may be loaded onto a user's mobile media player by creating one or more playlists from a playlist builder. The playlist(s) may be created by using user taste data. Rankings may also be created to determine an order for deletion of the media items currently on a mobile media player and/or for addition of new media items to the device.

Abstract: Systems and methods are disclosed for identifying a new set of media items in response to an input set (or “query set”) of media items and knowledge base metrics. The system uses a knowledge base consisting of a collection of mediasets. Various metrics among media items are considered by analyzing how the media items are grouped to form the mediasets in the knowledge base. Such association or “similarity” metrics are preferably stored in a matrix form that allows the system to efficiently identify a new set of media items that complements the input set of media items.

Abstract: A system to navigate through a media item catalog using correlation metrics (FIGS. 1,2) from a knowledge base (400) of mediasets (FIG. 4, 1-7). The system uses a knowledge base consisting of a collection of mediasets. Various metrics (Metric 1-Metric m) among media items (m1 . . . ) are considered by analyzing how the media items are grouped to form the sets in the knowledge base (400). Such metrics preferably are stored in a matrix (100) that allows the system to dynamically build appropriate navigation lists (FIG. 3) from media items that a user selects (FIG. 5).