Microsoft Technology Licensing, LLC

Deepak Kumar Dileep Kumar

Gungor Polatkan

Mahesh Subhash Joshi

Shivani Rao

Jeffrey Gee

Konstantin Salomatin

SeyedMohsen Jamali

Sneha S Chaudhari

Siyuan Zhang

Bonnie Bills

Vladislav Tcheprasov

Christopher Wright Lloyd, Ii

Zhe Cui

Techniques are provided for using machine learning techniques to associate skills with different content. In one technique, multiple classifications models are trained. Each classification model corresponds to a different skill and is trained based on textual embeddings of a plurality of content items and labels indicating whether each content item is associated with the skill that corresponds to that classification model. A particular content item embedding is generated based on text from a particular content item. The particular content item embedding is applied to the classification models to generate multiple results. One or more results of the multiple results are identified that indicate that one or more corresponding skills are associated with the particular content item. For each result of the one or more results, skill tagging data are stored that associate the particular content item with a particular skill that corresponds to that result.

Machine learning for associating skills with content

In an embodiment, the disclosed technologies include determining a digital identifier, computing, using aggregate digital event data obtained from at least one computing device, digital feature data relating to the digital identifier, inputting the digital feature data relating to the digital identifier into a digital model that has machine-learned correlations between digital feature data and digital propensity prediction values, receiving, from the digital model, a predicted propensity value associated with the digital identifier, determining a propensity score based on the predicted propensity value, causing a digital content item to be displayed on a user interface of a network-based software application associated with the digital identifier if the propensity score satisfies a propensity criterion.

Automatic modeling of online learning propensity for target identification

Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model.

Feature generation pipeline for machine learning

Growing community of inventors

Sunnyvale, CA, United States of America

Deepak Kumar Dileep Kumar