# US 11,443,255 B2 · Activity-based inference of title preferences

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- Number: US 11,443,255 B2
- Document ID: US11443255B2
- Status: Granted
- Kind: B2
- Application number: 16/185,365
- Filed: 2018-11-09
- Published: 2020-05-14
- Granted: 2022-09-13
- Earliest claimed: 2018-11-09
- Assignees: Microsoft Technology Licensing, LLC
- Applicants: Microsoft Technology Licensing, LLC
- Inventors: Ye Yuan; Girish Kathalagiri Somashekariah; Huichao Xue; Ada Cheuk Ying Yu
- Web page: https://patentgrep.com/patent/US11443255B2

## CPC

- G06Q 10/063112: {Skill-based matching of a person or a group to a task}
- G06N 5/04: Inference or reasoning models
- G06N 20/00: Machine learning
- G06Q 10/105: Human resources
- G06Q 10/1053: Employment or hiring
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

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.

## Claims

### Claim 1

A method, comprising:
determining, by one or more computer systems, features and labels for use in training a machine learning model, the features including a set of positively labeled features comprising at least i) job titles explicitly specified as preferred job titles by candidates in a first set of candidates, ii) job titles of job postings associated with job applications submitted by candidates in the first set of candidates, and iii) job titles of job postings selected for viewing by candidates in the first set of candidates, and the features including a set of negatively labeled features comprising at least randomly selected job titles;
inputting, by the one or more computer systems, the set of positively labeled features and the set of negatively labeled features as training data for training the machine learning model to predict for each candidate in a second set of candidates a preferred job title;
subsequent to training the machine learning model, applying, by the one or more computer systems, the trained machine learning model to features for the second set of candidates to produce predictions of preferred job titles for each candidate in the second set of candidates;
storing the predictions of preferred job titles in association with the second set of candidates;
generating job recommendations for each candidate in the second set of candidates, by:
inputting the predictions of preferred job titles with the features for the second set of candidates into a second, pre-trained machine learning model;
receiving, as output from the second, pre-trained machine learning model, scores representing likelihoods of the second set of candidates applying to jobs associated with job postings; and
generating the job recommendations for the second set of candidates based on rankings of the scores;
detecting an interaction, by each candidate, with the job recommendation; and
using data representing the detected interaction as additional training data to update the machine learning model to make improved predictions.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
outputting a subset of the predictions of job titles as recommended job title preferences for the second set of candidates.

### Claim 3 (depends on 1)

The method of claim 1, wherein the features for the second set of candidates comprise a job title associated with an application to a job by a candidate.

### Claim 4 (depends on 1)

The method of claim 1, wherein the features for the second set of candidates comprise a job title inputted into a job search query by a candidate.

### Claim 5 (depends on 1)

The method of claim 1, wherein the features for the second set of candidates comprise a similarity between a potential job title preference and a job title of a job posting associated with a previous interaction by a candidate.

### Claim 6 (depends on 1)

The method of claim 1, wherein the features for the second set of candidates comprise a job title associated with a sequence of actions by a candidate.

### Claim 7 (depends on 1)

The method of claim 1, wherein the machine learning model comprises a logistic regression model.

### Claim 8

A system, comprising:
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the system to:
determine features and labels for use in training a machine learning model, the features including a set of positively labeled features comprising at least i) job titles of job postings associated with job applications submitted by a first set of candidates, ii) job titles of job postings selected for viewing by a candidate in the first set of candidates, and iii) job titles explicitly specified as preferred job titles by candidates in a first set of candidates, and the features including a set of negatively labeled features comprising at least randomly selected job titles;
input the set of positively labeled features and the set of negatively labeled features as training data for training the machine learning model to predict for each candidate in a second set of candidates a preferred job title;
subsequent to training the machine learning model, apply the trained machine learning model to features for the second set of candidates to produce predictions of preferred job titles for the second set of candidates;
store the predictions of preferred job titles in association with the second set of candidates; and
generate job recommendations for each candidate in the second set of candidates based on the predictions of preferred job titles, by:
inputting the predictions of preferred job titles with the features for the second set of candidates into a second, pre-trained machine learning model;
receiving, as output from the second, pre-trained machine learning model, scores representing likelihoods of the second set of candidates applying to jobs associated with job postings; and
generating the job recommendations for the second set of candidates based on rankings of the score;
detect an interaction, by each candidate, with the job recommendation; and
use data representing the detected interaction as additional training data to update the machine learning model to make improved predictions.

### Claim 9 (depends on 8)

The system of claim 8, wherein the memory further stores instructions that, when executed by the one or more processors, cause the system to:
output a subset of the predictions of preferred job titles as recommended job title preferences for the second set of candidates.

### Claim 10 (depends on 8)

The system of claim 8, wherein the features for the second set of candidates comprise a similarity between a potential job title preference and a job title of a job posting associated with a previous interaction by a candidate.

### Claim 11

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:
determining, by one or more computer systems, features and labels for use in training a machine learning model, the features including a set of positively labeled features comprising at least i) job titles of job postings associated with job applications submitted by candidates in the first set of candidates, ii) job titles of job postings selected for viewing by candidates in the first set of candidates, and iii) job titles explicitly specified as preferred job titles by candidates in a first set of candidates, and the features including a set of negatively labeled features comprising at least randomly selected job titles;
inputting, by the one or more computer systems, the set of positively labeled features and the set of negatively labeled features as training data for training the machine learning model to predict for each candidate in a second set of candidates a preferred job title;
subsequent to training the machine learning model, applying, by the one or more computer systems, the trained machine learning model to features for the second set of candidates to produce predictions of preferred job titles for each candidate in the second set of candidates;
storing the predictions of preferred job titles in association with the second set of candidates;
generating job recommendations fora each candidate in the second set of candidates, by:
inputting the predictions of preferred job titles with the features for the second set of candidates into a second, pre-trained machine learning model;
receiving, as output from the second, pre-trained machine learning model, scores representing likelihoods of the second set of candidates applying to jobs associated with job postings; and
generating the job recommendations for the second set of candidates based on rankings of the scores;
detecting an interaction, by each candidate, with the job recommendation; and
using data representing the detected interaction as additional training data to update the machine learning model to make improved predictions of preferred job titles.