Microsoft Technology Licensing, LLC

Junrui Xu

Qing Duan

Huichao Xue

Xiaowen Zhang

Xiaoqing Wang

Ada Cheuk Ying Yu

Girish Kathalagiri Somashekariah

Ye Yuan

Jianqiang Shen

Varun Mithal

In an example embodiment, a first machine learned model is trained to produce output, and a second machine learned model is then trained using training data that has been labeled, at least partially, using the output of the first machine learned model. The first machine learned model is trained to output a measure of how strong a positive signal in the training data really is. Specifically, this measure indicates the level of intention of a user who has engaged in a first user interface action with respect to a piece of content to engage in a subsequent second user interface action with the same piece of content.

Click intention machine learned models

Technologies for generating a graph containing clusters of feature attribute values for training a machine learning model for content item selection and delivery are provided. The disclosed techniques include, for each entity, of a plurality of entities, a system identifies transitions from one geographic location to another geographic location. A graph is generated based on the transitions associated with each entity. The graph comprises nodes representing geographic locations and edges connecting the nodes. Each of the edges connects two nodes, represents a transition from one geographic location to another geographic location, and each edge represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two connected nodes. The system generates a plurality of clusters from the nodes based upon the edge weight value of each edge. The system includes the plurality of clusters as features in a machine learning model.

Location dimension reduction using graph techniques

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

In an example the output of a machine learned model is a score is then compared to a threshold, and if the score transgresses the threshold, the corresponding item is available to be recommended to the user via the graphical user interface. In an example embodiment, rather than a fixed (static) threshold, a dynamic threshold is utilized. This dynamic threshold is based on a harmonic mean of probabilities utilized in the GLMix model. Specifically, the GLMix model may calculate and utilize the probability that a user will engage with a particular item via a graphical user interface, and also a probability that a user will dismiss a particular item via a graphical user interface.

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Fremont, CA, United States of America

Junrui Xu