Abstract: Meaningful words or phrases may be extracted from the information and used as tags. Weights may be determined for the tags, and tag clouds may be generated for the items. The tag clouds may be stored to a data store. Information specifying a tag cloud may be received. Recommended items for which the tag clouds most closely match the specified tag cloud may be identified. Standard vector space distance calculations, for example the cosine distance between the tag clouds, may be used to determine cloud similarity. The results may be filtered to optimize relevance, novelty and familiarity in accordance with preferences of the user. The recommended items may be displayed to a user interface. Users may interact with the user interface to steer the recommendations towards more relevant content.

Abstract: A system provides a model for use within a digital environment. The model comprises at least one music segment, and supplies music for the digital environment. The system identifies a location within the digital environment. The location requires at least one music segment supplied by the model. The system selects at least one music segment to play within the digital environment. The music segment is selected based on the model, the location requiring the at least one music segment, and the digital environment. The selecting of the music segment is operable to be modified based on the model, the location requiring the at least one music segment, and the digital environment.

Abstract: Method and apparatus for transparent, steerable recommendations. A steerable recommender uses tag clouds including descriptive tags and associated weights to generate recommendations. Users may dynamically interact with the recommender via a user interface to steer the recommendations. A tag cloud for an item is displayed, items for which associated tag clouds are most similar to the displayed tag cloud are identified, and the items are displayed as recommendations. The strength of similarity of the items to the displayed tag cloud may be displayed. The user may modify a tag cloud, for example by changing the weight of a tag or by adding or removing a tag, and the recommendations may be automatically updated to reflect the modification. A recommended item may be selected to display the tag cloud corresponding to the item. A user may select a user interface element to request information on why a particular item was recommended.

Abstract: Example embodiments herein include an audio manager that identifies at least one user in a social environment (e.g., multiplayer gaming environment). Each user in the social environment has a respective user music profile that contains audio attributes for various types of audio. The audio manager combines the user music profile from each user to form a group music profile. The audio manager further identifies a selection of audio segments to be rendered in the social environment that match the group music profile. Additionally, the audio manager processes the selection of audio segments against the audio attributes of at least one user to determine an audio segment to be rendered in the social environment. In one embodiment, the audio manager processes the selection of audio segments in accordance with a social value metric. In turn, the audio manager renders the audio segment in the social environment in accordance with the social value metric.

Abstract: A fingerprint and indexing service is implemented to receive a media object and generate a fingerprint corresponding to the media object. The fingerprint and indexing service may segment the fingerprint into frames and generate a confidence value for each bit within each frame. The confidence values may be added together and totaled, such that each frame has a corresponding confidence score. The frames may be ranked, according to their confidence scores. N of the top ranked frames may be selected as the index. Subsequently, a search component may determine the index values for a query media object. The database of media objects may be searched for matching index values for fingerprints with fingerprint lengths matching the query media object's length. Once a match is declared, the fingerprints of the query media object and the matching media objects within the database may be compared to determine if a match exists.

Abstract: A method for accessing a file system including computing a first numerical similarity score for a first stored document and a second numerical similarity score for a second stored document by comparing a plurality of weighted active terms with a plurality of weighted indexed terms, determining a document order of the first stored document followed by the second stored document based on the first numerical similarity score exceeding the second numerical similarity score, generating a list of similar documents including the first stored document followed by the second stored document based on the document order, and displaying, in a file system interface and on the computer display, the list of similar documents while an active document is open in an active document interface.

Abstract: A method for searching and resource discovery in a distributed enterprise (DE), the method including: generating a first classifier for a first repository in the DE; generating a second classifier for a second repository in the DE, where the second classifier has a vector element identifying a location of the second repository; submitting a copy of the second classifier to a web server of the first repository; obtaining a resource at the first repository after submitting the copy of the second classifier; matching the resource to the copy of a second classifier; sending a copy of the resource to the second repository using the vector element; and storing the copy of the resource in the second repository.

Abstract: A method for accessing a file system including computing a first numerical similarity score for a first stored document and a second numerical similarity score for a second stored document by comparing a plurality of weighted active terms with a plurality of weighted indexed terms, determining a document order of the first stored document followed by the second stored document based on the first numerical similarity score exceeding the second numerical similarity score, generating a list of similar documents including the first stored document followed by the second stored document based on the document order, and displaying, in a file system interface and on the computer display, the list of similar documents while an active document is open in an active document interface.

Abstract: A method for accessing a file system that includes creating a document index for an active document, identifying a similarity score for each of a plurality of stored documents to generate a list of similar documents, wherein the similarity score is computed by comparing the document index for the active document with a search index for the plurality of stored documents, and displaying the list of similar documents in a file system interface.

Abstract: Method and apparatus for transparent, steerable recommendations. A steerable recommender uses tag clouds including descriptive tags and associated weights to generate recommendations. Users may dynamically interact with the recommender via a user interface to steer the recommendations. A tag cloud for an item is displayed, items for which associated tag clouds are most similar to the displayed tag cloud are identified, and the items are displayed as recommendations. The strength of similarity of the items to the displayed tag cloud may be displayed. The user may modify a tag cloud, for example by changing the weight of a tag or by adding or removing a tag, and the recommendations may be automatically updated to reflect the modification. A recommended item may be selected to display the tag cloud corresponding to the item. A user may select a user interface element to request information on why a particular item was recommended.

Abstract: Meaningful words or phrases may be extracted from the information and used as tags. Weights may be determined for the tags, and tag clouds may be generated for the items. The tag clouds may be stored to a data store. Information specifying a tag cloud may be received. Recommended items for which the tag clouds most closely match the specified tag cloud may be identified. Standard vector space distance calculations, for example the cosine distance between the tag clouds, may be used to determine cloud similarity. The results may be filtered to optimize relevance, novelty and familiarity in accordance with preferences of the user. The recommended items may be displayed to a user interface. Users may interact with the user interface to steer the recommendations towards more relevant content.

Abstract: A method for recommending music that includes identifying a granularity of a plurality of genres based on a request for music similarity, wherein the request identifies a user, training a genre classifier based on the granularity to obtain a trained genre classifier, calculating a first profile by the trained genre classifier, wherein the first profile that includes, for each of the plurality of genres, the likelihood that a music selection associated with a user is in the genre, calculating a second profile by the trained genre classifier, wherein the second profile that includes, for each of the plurality of genres, the likelihood that an unknown music selection is in the genre, obtaining a first similarity score between the first profile and a second profile, and recommending the unknown music selection to the user based on the first similarity score.

Abstract: Method and apparatus for programmatically generating interesting audio file playlists. A playlist generation mechanism may use an N-gram model of audio file ordering patterns found in a collection of human-generated playlists to automatically generate new playlists. Given play histories indicating one or more played audio files as input, statistical methods may be used to look for sequences of audio files that occur a statistically significant number of times in the N-gram model for inclusion in new, interesting playlists that incorporate the human element found in the collection of playlists. In some embodiments, one more backoff probability methods may be used to provide additional candidate audio files for playlists if there is insufficient coverage for an audio file in the N-gram model. In one embodiment, a class-based statistical model incorporating higher-level statistics for the audio files may be used to weight selection of audio file transitions from the N-gram model.

Abstract: A method for searching and resource discovery in a distributed enterprise (DE), the method including: generating a first classifier for a first repository in the DE; generating a second classifier for a second repository in the DE, where the second classifier has a vector element identifying a location of the second repository; submitting a copy of the second classifier to a web server of the first repository; obtaining a resource at the first repository after submitting the copy of the second classifier; matching the resource to the copy of a second classifier; sending a copy of the resource to the second repository using the vector element; and storing the copy of the resource in the second repository.

Abstract: A media playlist is generated from a set of two or more seed media selections as well as the desired number of selections in the playlist. The seed selections represent waypoints in “media space”. A playlist is generated by finding a path of “stepping stone” selections between the waypoints, with the constraints that no selection is presented more than once and the maximum distance between stepping stones is minimized. In one embodiment, an “endless” playlist by adding a final path from the last playlist selection back to a selection that is nearest the first selection, but has not been presented yet. In still another embodiment, a separate set of “avoid” selections is maintained. When generating the playlist, given a set of paths that meet the aforementioned path generation constraints, paths will be selected that maximize the distance from the set of “avoid” selections.

Abstract: A fingerprint and indexing service is implemented to receive a media object and generate a fingerprint corresponding to the media object. The fingerprint and indexing service may segment the fingerprint into frames and generate a confidence value for each bit within each frame. The confidence values may be added together and totaled, such that each frame has a corresponding confidence score. The frames may be ranked, according to their confidence scores. N of the top ranked frames may be selected as the index. Subsequently, a search component may determine the index values for a query media object. The database of media objects may be searched for matching index values for fingerprints with fingerprint lengths matching the query media object's length. Once a match is declared, the fingerprints of the query media object and the matching media objects within the database may be compared to determine if a match exists.

Abstract: A method for accessing a file system that includes creating a document index for an active document, identifying a similarity score for each of a plurality of stored documents to generate a list of similar documents, wherein the similarity score is computed by comparing the document index for the active document with a search index for the plurality of stored documents, and displaying the list of similar documents in a file system interface.

Abstract: A system provides a model for use within a digital environment. The model comprises at least one music segment, and supplies music for the digital environment. The system identifies a location within the digital environment. The location requires at least one music segment supplied by the model. The system selects at least one music segment to play within the digital environment. The music segment is selected based on the model, the location requiring the at least one music segment, and the digital environment. The selecting of the music segment is operable to be modified based on the model, the location requiring the at least one music segment, and the digital environment.

Abstract: A method for recommending music that includes identifying a granularity of a plurality of genres based on a request for music similarity, wherein the request identifies a user, training a genre classifier based on the granularity to obtain a trained genre classifier, calculating a first profile by the trained genre classifier, wherein the first profile that includes, for each of the plurality of genres, the likelihood that a music selection associated with a user is in the genre, calculating a second profile by the trained genre classifier, wherein the second profile that includes, for each of the plurality of genres, the likelihood that an unknown music selection is in the genre, obtaining a first similarity score between the first profile and a second profile, and recommending the unknown music selection to the user based on the first similarity score.

Abstract: In a distributed enterprise computing system, personal data repositories are created by individual users who generate classifiers that index the information for those repositories. Each generated classifier also specifies a target repository into which a copy of a resource that matches that classifier is placed. Users can submit classifiers that they created to other repositories. Later, when a user adds a resource to his personal repository, it is checked against all classifiers that have been created by the user and submitted to the repository by other users. If the new resource matches any classifier, a copy of the resource is sent to the repository specified in the classifier, where the resource is archived and indexed.