Microsoft Technology Licensing, LLC

Revant Kumar

SeyedMohsen Jamali

Samaneh Abbasi Moghaddam

Jinyun Yan

Kinjal Basu

Onkar Anant Dalal

Zhiyuan Xu

Vinay Praneeth Boda

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 automatically tuning a parameter in a layered model framework. One or more machine learning techniques are used to train multiple versions of a first model that includes a first version and a second version. A second model is stored that includes a parameter and accepts, as input, output from the first model. Multiple parameter values of the parameter are tested when processing content requests using the first and second versions of the first model. A strict subset of the plurality of parameter values are selected for the parameter of the second model, such that processing a first subset of the content requests using the first version of the first model results in a first value of a particular metric that matches a second value of the particular metric resulting from processing a second subset of the content requests using the second version of the first model.

Automatically tuning parameters in a layered model framework

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Sunnyvale, CA, United States of America

Revant Kumar