Facebook, Inc. (Menlo Park, CA)

The current disclosure relates to a high-capacity training and prediction machine-learning platform that is able to support high-capacity parameter models (e.g. 10 billion parameters). The platform generates a model for a metric of interest based on a known training set. The model is based on parameters that indicate the how important different aspects of the model, which can be viewed both singly and in pairs. The model can be used to estimate the significance of a metric for specific objects, for instance an object that is a user as well as content items.

Machine learning models are utilized by many companies to analyze huge, complicated data sets (“bigdata”) and provide important services to customers. A social network service could create a social network to provide users with personalized or targeted services using big data. Big data is a broad term referring to the use of predictive techniques to extract values from large datasets. They are usually so complex that traditional data processing systems often fail for providing the relevant information. For instance, analyzing the data can reveal new patterns, trends, patterns, categories, etc. This could be between, for instance, a user, and an item or service. But, the current computer systems typically have computing capacities that can handle only small models with a limited range of parameters (e.g., 10 million parameters). On the other hand, systems capable of processing a larger set of parameters usually require significant time, memory, and CPUusage.

A machine learning system with high capacity creates a model to predict the value of a corresponding metric based on a given set of characteristics. Parameters.alpha..sub.f.f(i),f(j), which indicate the importance of certain pair of features to the metrics value and are included in the model. Additional parameters can be added to the model to indicate the importance of different elements in isolation, based on the metrics value. A matrix of embedding can be utilized to represent variables’ interactions. Use of the parameters.alpha..sub.f(i), f(j) advantageously permits greater model accuracy, with only a small increase in the amount of required memory.

These features and benefits aren’t all-inclusive. Anyone with average proficiency in art will be able to see numerous other advantages and features in the claims, drawings, and the specification. It is also important to note that the language in the specification was chosen principally to be readable and for an instructional purpose and may not have been used to define or circumscribe the inventive subject matter.

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