Microsoft Technology Licensing, LLC

Fares Hedayati

Mahesh Subhash Joshi

Gungor Polatkan

Sneha S Chaudhari

Gautam Borooah

Young Jin Yun

Jeffrey Gee

Deepak Kumar

Shivani Rao

Konstantin Salomatin

Bonnie Bills

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

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.

Grouping a first set of video items to efficiently match against a second set of video items using embedding similarity

The video recommendation system provided with an on-line connection system generates on-line video recommendations using collaborative filtering for clusters of member profiles. The recommendation system clusters member profiles using member profile information as clustering criteria. The video recommendations are then generated for a given cluster, based on aggregation of video viewing history recorded for the member profiles that are in the given cluster, using the video similarity matrix. In order to produce video recommendations for a particular member profile, the recommendation system first determines cluster membership for the member profile, retrieves recommendations generated for that cluster, and provides recommendations to the associated member. A user interface including references to one or more recommended videos is rendered on a display device of a viewer.

Cluster-based collaborative filtering

Techniques for classifying videos as standalone or non-standalone are provided. Feature (or attribute) values associated with a particular video are identified. Feature values are extracted from metadata associated with the particular video and/or from within a transcript of the particular video. The extracted feature values of the particular video are input to a rule-based or a machine-learned model and the model scores the particular video. Once a determination pertaining to whether the particular video is standalone is made, information about the particular video being a standalone video is presented to one or more users within the network.

Standalone video classification

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Fares Hedayati