Microsoft Technology Licensing, LLC

Huichao Xue

Ada Cheuk Ying Yu

Girish Kathalagiri Somashekariah

Ye Yuan

Junrui Xu

Chao Wang

Dhruv Arya

Qing Duan

Xiaoqing Wang

Kevin Kao

Varun Mithal

Jianqiang Shen

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

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

Systems and methods for classifying job search queries for improved precision using a machine-trained classifier are provided. In example embodiments, a network system receives a job search query including one or more terms from a device of a user. The network system extracts one or more features from the job search query, whereby the one or more features are derived from the one or more terms. Based on the one or more features, a machine-learned model of the classifier determines whether to use a title field search process or a compound search process. Based on the determining, the network system formats the job search query into a corresponding machine-language format and performs the job search query to derive results. The network system causes presentation of the results on the device of the user.

Classifying job search queries for improved precision

The disclosed embodiments provide a system for performing activity-based inference of title preferences. During operation, the system determines features and labels related to first title preferences for jobs sought by a first set of candidates. Next, the system inputs the features and the labels as training data for a machine learning model. The system then applies the machine learning model to additional features for a second set of candidates to produce predictions of second title preferences for the second set of candidates. Finally, the system stores the predictions in association with the second set of candidates.

Activity-based inference of title preferences

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

Methods, systems, and computer programs are presented for search optimization based on relevant-parameter selection. One method includes an operation for training a machine-learning program with information about users of an online service to generate a machine-learning model that calculates parameter preference scores for a plurality of parameters. Further, the method includes operations for detecting a job search for a user, identifying user parameters associated with the user, and calculating, by the machine-learning model, the parameter preference scores for the user parameters. Further, search parameters are determined by selecting a predetermined number of user parameters base on the parameter preference scores. A search of a job-postings database is performed with the search parameters, and the results are presented on a display.

Search optimization based on relevant-parameter selection

This disclosure relates to systems and methods for recommending relevant positions. A method includes receiving, from a member of an online networking service, a query for one or more available employment positions; executing the query, at a database of employment positions, to retrieve the one or more available employment positions; filtering results of the query according to one or more facets; generating an electronic user interface to display the filtered results; and allowing the member to adjust the facets using the electronic user interface.

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Santa Clara, CA, United States of America

Huichao Xue