Microsoft Technology Licensing, LLC

Sneha S Chaudhari

Mahesh Subhash Joshi

Gungor Polatkan

Fares Hedayati

Gautam Borooah

Young Jin Yun

Deepak Kumar Dileep Kumar

Efficient tagging of content items using content embeddings are provided. In one technique, multiple content items are stored a content embedding for content item is stored. Entity names are also stored along with an entity name embedding for each entity name. For each content item, (1) multiple content embeddings that are associated with the content item are identified; (2) a subset of the entity names is identified; and (3) for each entity name in the subset, (i) an embedding of the entity name is identified, (ii) similarity measures are generated based on the entity name embedding and the multiple content embeddings, (iii), a distribution of the similarity measures is generated, (iv) feature values are generated based on the distribution, (v) the feature values are input into a machine-learned classifier, and (vi) based on output from the classifier, it is determined whether to associate the entity name with the content item.

Efficient tagging of content items using multi-granular embeddings

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 efficiently matching two sets of video items are provided. In on technique, an embedding is generated for each video item in each set. For the first set of video items, multiple groups are generated. The first set of video items may have a relatively little amount of metadata information for them. Each video item in the first set is assigned to one of the groups. Then, for each video item in the second set, one of the groups is selected based on embedding similarity. For each video item in the selected group, an embedding similarity is determined between that video item in the selected group and the video item in the second set. If the embedding similarity is above a certain threshold, then an association is generated for that pair of video items.

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Sunnyvale, CA, United States of America

Sneha S Chaudhari