Microsoft Technology Licensing, LLC

SeyedMohsen Jamali

Samaneh Abbasi Moghaddam

Revant Kumar

Gungor Polatkan

Xiaowen Zhang

Xiaoqing Wang

Shivani Rao

Deepak Kumar Dileep Kumar

Siyuan Zhang

Vinay Praneeth Boda

Bonnie Bills

Zhe Cui

Ali Abbasi

Techniques are provided for using machine learning techniques to learn embeddings for content items. In one technique, training data is used to learn embeddings for each attribute value of multiple attribute values of multiple content items, embeddings for each attribute value of multiple attribute values of multiple entities, and weights for a set of contextual features. In response to receiving a content request, a content item that is associated with one or more targeting criteria that are satisfied based on the content request is identified. A first set of embeddings for the content item are identified, a requesting entity that initiated the content request is identified along with a second set of embeddings for the requesting entity, and a set of feature values for the set of contextual features is identified. The content item is selected based on the sets of embeddings, the set of feature values, and the weights.

Embedded learning for response prediction in content item relevance

Techniques for using online engagement footprints for video engagement prediction are provided. In one technique, events are received from multiple client devices, each event indicating a type of engagement of a video item from among multiple types of engagement. One or more machine learning techniques are used to train a prediction model that is based on the events and multiple features that includes the multiple types of engagement. In response to receiving a content request, multiple entity feature values are identified for a particular entity that is associated with the content request. Two or more of the entity feature values correspond to two or more of the types of engagement. A prediction is generated based on the entity feature values and the prediction model. The prediction is used to determine whether to select, from candidate content items, a particular content item that includes particular video.

Using online engagement footprints for video engagement prediction

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 example embodiment, cohort-based generalized linear mixed effect model (GLMIX) training is performed to identify patterns across cohorts of users, rather than slicing across all users blindly without accounting for common characteristics of users. Thus, rather than performing GLMIX training at just the finest granular level (e.g., user-level and job-level) or the highest level (global level), a 'medium' level of granularity is used to train the GLMIX model at cohort-level.

Cohort-based generalized linear mixed effect model

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Sunnyvale, CA, United States of America

SeyedMohsen Jamali