Microsoft Technology Licensing, LLC

Varun Mithal

Ye Yuan

Aman Grover

Ada Cheuk Ying Yu

Girish Kathalagiri Somashekariah

Vita G Markman

Huichao Xue

Junrui Xu

Girish Kathalagiri Somashekairah

Igor Vladimir Yagolnitser

Machine learning based method for multilabel learning with label relationships is provided. This methodology addresses the technical problem of alleviating computational complexity of training a machine learning model that generates multilabel output with constraints, especially in contexts characterized by a large volume of data, by providing a new formulation that encodes probabilistic relationships among the labels as a regularization parameter in the training objective of the underlying model. For example, the training process of the model may be configured to have two objectives. Namely, in addition to the objective of minimizing conventional multilabel loss, there is another training objective, which is to minimize penalty associated with the prediction generated by the model breaking probabilistic relationships among the labels.

Multilabel learning with label relationships

In some embodiments, a computer system selects a first subset of candidate content items based on their filter scores that are generated based on a partial generalized linear mixed model comprising a baseline model and a user-based model, with the baseline model being a generalized linear model, and the user-based model being a random effects model based on user actions by the target user directed towards reference content items related to the candidate content items. In some embodiments, the computer system then selects a second subset from the first subset based on recommendation scores that are generated based on a full generalized linear mixed model comprising the baseline model, the user-based model, and an item-based model, with the item-based model being a random effects model based on user actions directed towards the candidate online content item by reference users related to the target user.

Filtering content using generalized linear mixed models

Techniques for using online user activity in determining relevance of attributes to improve computer functionality in generating recommendations of online content are disclosed herein. In some embodiments, a computer system calculates a corresponding relevance score for each attribute of a user based on a total number of online postings for which the user has performed at least one of a plurality of online actions within a particular sliding window of time defining a most recent time period, an attribute activity number representing a number of online postings in the plurality of online postings that have the attribute, and an inverse of a frequency value representing how many of a total number of online postings published within the particular sliding window of time have the attribute. In some embodiments, the computer system causes at least one recommendation associated with the user to be displayed based on the calculated relevance scores.

System user attribute relevance based on activity

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Sunnyvale, CA, United States of America

Varun Mithal