Abstract:
Methods, systems and computer program products are provided for predicting data. A name or title is obtained from a taste profile. There is an index into a data set based on the name or title, and a set of terms and corresponding term weights associated with the name or title are retrieved. A sparse vector is constructed based on the set of terms and term weights. The sparse vector is input to a training model including target data. The target data includes a subset of test data which has a correspondence to a predetermined target metric of data. A respective binary value and confidence level is output for each term, corresponding to an association between the term and the target metric.
Abstract:
Methods, systems and computer program products are provided for providing content recommendation by obtaining metadata associated with a media object, extracting from the metadata a plurality of terms associated with the media object, and mapping at least a portion of the plurality of terms to buckets. A query vector having attributes corresponding to the buckets is used to perform a query on a database storing media object documents having attributes corresponding to the buckets.
Abstract:
A catalog record is bridged to information stored in at least one inverted index by receiving an application user interface call associated with a predetermined filter request including a record identifier identifying a record in a relational database. A bitset is generated based on item identifiers in the record. The bitset is applied to at least one inverted index to obtain metadata associated with the item identifiers.
Abstract:
Methods, systems and computer program products are provided for summarizing user activity associated with media content by accessing a taste profile containing a representation of media content activity corresponding to at least one of a plurality of items, generating at least one statistic corresponding to the media content activity, and generating a taste profile attribute by using the at least one statistic.
Abstract:
A data processing method, program, and apparatus for identifying a document within a block of text. A block of text is tokenized into a plurality of text tokens according to at least one rule parser. Each of the plurality of text tokens is sequentially compared to a plurality of document tokens to determine if the text token matches one of the plurality of document tokens. The plurality of document tokens correspond to a plurality of documents which have been tokenized according to the one or more rule parsers. Each matched text token is filtered according to predetermined filtering criteria to generate one or more candidate text tokens. It is then determined whether sequence of candidate text tokens that occur in sequential order within the block of text match sequence of document tokens. If so, then it is determined that the document has been identified within the block of text. The document can correspond to an artist, a song names, and misspellings and aliases thereof.
Abstract:
Methods, systems and computer program products are provided for predicting data. A name or title is obtained from a taste profile. There is an index into a data set based on the name or title, and a set of terms and corresponding term weights associated with the name or title are retrieved. A sparse vector is constructed based on the set of terms and term weights. The sparse vector is input to a training model including target data. The target data includes a subset of test data which has a correspondence to a predetermined target metric of data. A respective binary value and confidence level is output for each term, corresponding to an association between the term and the target metric.
Abstract:
Methods, systems and computer program products are provided for cross-media recommendation by store a plurality of taste profiles corresponding to a first domain and a plurality of media item vectors corresponding to a second domain. An evaluation taste profile in the first domain is applied to a plurality of models that have been generated based on relationship among the plurality of taste profiles and the plurality of media item vectors, and obtain a plurality of resulting codes corresponding to at least one of the plurality of media item vectors in the second domain.
Abstract:
Methods, systems and computer program products are provided for generating a playlist. An application programming interface (API) receives a request to generate a playlist, where the request includes a set of rule-primitives. A playlist engine evaluator evaluates a rule corresponding to each rule-primitive in the set of rule-primitives across a catalog of media content, calculates a cost associated with each item in the catalog of media content, and generates a playlist based on the items of the catalog having the lowest costs.