Microsoft Technology Licensing, LLC

Box Limited

Shivani Rao

Gungor Polatkan

Jeffrey Gee

Mahesh Subhash Joshi

Konstantin Salomatin

Deepak Kumar

Deepak Kumar Dileep Kumar

Adelbert Chang

Fares Hedayati

SeyedMohsen Jamali

Sivaramakrishnan Subramanian

Siyuan Zhang

Bonnie Bills

Christopher Wright Lloyd, Ii

Yulia Astakhova

Divya Jain

Lance Co Ting Keh

Vladislav Tcheprasov

Zhe Cui

Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.

Method and system for implementing machine learning analysis of documents for classifying documents by associating label values to the documents

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

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

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

The recommendation system provided with an on-line connection system identifies on-line recommendations of videos and generates a user interface (UI) by including into the resulting presentation selected recommendations of videos. The recommendations of videos presented in the UI are organized into groups that are topically coherent, where each group is decorated with a context annotation&#x2014;an explanation of why the recommendations in a given carousel are relevant for a member. Each video that is being evaluated by the recommendation system with respect to a subject member profile is assigned an annotation that is selected from a plurality of potentially applicable annotations. The technical problem of optimizing an order of presentation of recommendations grouped by context annotations, in a UI where annotations drive the layout, is addressed by deriving ranks for different context annotations based on global and personalized click through rates and using these values assigned to respective context annotations in constructing the UI.

Ranking carousels of on-line recommendations of videos

Growing community of inventors

Mountain View, CA, United States of America

Shivani Rao