Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.

Abstract: Parallel processing of data may include a set of map processes and a set of reduce processes. Each map process may include at least one map thread. Map threads may access distinct input data blocks assigned to the map process, and may apply an application specific map operation to the input data blocks to produce key-value pairs. Each map process may include a multiblock combiner configured to apply a combining operation to values associated with common keys in the key-value pairs to produce combined values, and to output intermediate data including pairs of keys and combined values. Each reduce process may be configured to access the intermediate data output by the multiblock combiners. For each key, an application specific reduce operation may be applied to the combined values associated with the key to produce output data.

Abstract: Implementations of the disclosed subject matter provide methods and systems for using a multistage learner for efficiently boosting large datasets in a machine learning system. A method may include obtaining a first plurality of examples for a machine learning system and selecting a first point in time. Next, a second point in time occurring subsequent to the first point in time may be selected. The machine learning system may be trained using m of the first plurality of examples. Each of the m examples may include a feature initially occurring after the second point in time. In addition, the machine learning system may be trained using n of the first plurality of examples, and each of the n examples may include a feature initially occurring after the first point in time.

Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.

Abstract: Systems and techniques are disclosed for generating weighted machine learned models using multi-shard combiners. A learner in a machine learning system may receive labeled positive and negative examples and workers within the learner may be configured to receive either positive or negative examples. A positive and negative statistic may be calculated for a given feature and may either be applied separately in a model or may be combined to generate an overall statistic.

Abstract: Parallel processing of data may include a set of map processes and a set of reduce processes. Each map process may include at least one map thread. Map threads may access distinct input data blocks assigned to the map process, and may apply an application specific map operation to the input data blocks to produce key-value pairs. Each map process may include a multiblock combiner configured to apply a combining operation to values associated with common keys in the key-value pairs to produce combined values, and to output intermediate data including pairs of keys and combined values. Each reduce process may be configured to access the intermediate data output by the multiblock combiners. For each key, an application specific reduce operation may be applied to the combined values associated with the key to produce output data.

Abstract: A system and method for generating a model based on the user's interests and activities by receiving with a logging unit user activities from heterogeneous data sources, generating a log of user activities for a content item by joining the user activities for the content item, expanding attributes of the log by at least one of content and by the user to form an expanded log and generating a user model based on the expanded log. A feature extractor extracts features from content items and assigns weights to the features. A scoring engine receives the model and the content items with their associated weighted features and scores the content items based on the user model. The scoring engine generates a stream of content based on the scored content items.

Abstract: Implementations of the disclosed subject matter provide methods and systems for using a multistage learner for efficiently boosting large datasets in a machine learning system. A method may include obtaining a first plurality of examples for a machine learning system and selecting a first point in time. Next, a second point in time occurring subsequent to the first point in time may be selected. The machine learning system may be trained using m of the first plurality of examples. Each of the m examples may include a feature initially occurring after the second point in time. In addition, the machine learning system may be trained using n of the first plurality of examples, and each of the n examples may include a feature initially occurring after the first point in time.

Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.

Abstract: A machine learning system to rank data within sets is disclosed. The system comprises a ranking module that has differentiable parameters. The system further comprises a cost calculation module that uses a cost function that depends on pairs of examples and which describes an output of the ranking module. Methods of using the disclosed system are also provided.

Abstract: A machine learning system to rank data within sets is disclosed. The system comprises a ranking module that has differentiable parameters. The system further comprises a cost calculation module that uses a cost function that depends on pairs of examples and which describes an output of the ranking module. Methods of using the disclosed system are also provided.