Microsoft Technology Licensing, LLC

Xiaoqing Wang

Xiaowen Zhang

Qing Duan

Junrui Xu

Chao Wang

Samaneh Abbasi Moghaddam

Huichao Xue

SeyedMohsen Jamali

Huizhen Ji

Xin Bai

In an example embodiment, machine learning is used to train a machine-learned model that projects each entity, title pair into a single number, called a seniority score, to represent the career progression needed for that position. For example, company A's 'software engineer' and company B's “senior software engineer” can be represented as two separate numbers, one being p (company A, software engineer) and the other being p (company B, senior software engineer) on the same axis. This allows a comparison to be made about the absolute levels of each title despite their potential different meanings at different entities.

Machine learning to infer title levels across entities

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 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

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.

Personalized model threshold

A user enters a query into an element of a user interface. Query suggestions that are based on the query are displayed around the query in the user interface. Each of the query suggestions is displayed in an element of the user interface that indicates a rank of the query suggestion to the user. When the user holds or hovers over a particular element of the user interface, a pop-up window is displayed with the search results that are responsive to the query or query suggestion that is associated with the element, allowing the user to compare the query and the query suggestions. In addition, the user may rotate the elements associated with the query suggestions displayed around the element associated with the query in the user interface to indicate that they would like to receive additional or different query suggestions.

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San Jose, CA, United States of America

Xiaoqing Wang