# US 2020/0402012 A1 · Multi-objective optimization of job applications redistribution > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0402012 A1 - Document ID: US20200402012A1 - Status: Published application - Kind: A1 - Application number: 16/445,862 - Filed: 2019-06-19 - Published: 2020-12-24 - Earliest claimed: 2019-06-19 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC. - Inventors: Chunzhe Zhang; Krishnaram Kenthapadi; Boyu Zhang; Bimal Sundaran Parakkal; Hai Jian Guan - Web page: https://patentgrep.com/patent/US20200402012A1 ## CPC - G06Q 10/1053: Employment or hiring - G06F 16/24578: {using ranking} - G06F 16/9035: Filtering based on additional data, e.g. user or group profiles - G06N 20/00: Machine learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 20/20: Ensemble learning - 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 - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A technical problem of multi-objective optimization of job applications redistribution in an online connection network system is addressed by incorporating monetary value of job applications as a signal into a ranker for ranking jobs with respect to a member profile in job search and recommendations. The monetary value of job applications is used in addition to the relevance signal and is determined by executing a machine learning model that accounts for the covariates that could impact monetary value of an application for a job. ## Claims ### Claim 1 A computer implemented method comprising: accessing a member profile that represents a member in an online connection network and a set of candidate job postings maintained by the online connection network, each job in the set of candidate job postings is an electronic listing of a job opening comprising job features; for jobs from the set of candidate job postings: determining a value of a next application for the job, executing a relevance machine learning model to predict relevance of the job, calculating a rank of the job by multiplying the relevance of the job by the value of the next application for the job; and including a subset of jobs from the set of candidate job postings, based on their respective ranks, into a user interface (UI) for presentation to the member on a display device. ### Claim 2 (depends on 1) The method of claim 1, wherein the determining of the value of the next application for the job comprises: executing an application value machine learning model to predict projected value associated with a job poster of the job; and calculating the value of the next application for the job as the difference between the projected value associated with the job poster of the job and a previously-predicted projected value associated with the job poster of the job. ### Claim 3 (depends on 2) The method of claim 2, comprising using a number of previously occurred applications for the job, features of the job and features of the job poster as input to the application value machine learning model. ### Claim 4 (depends on 2) The method of claim 2, wherein the application value a e learning model is a linear-log mixed model. ### Claim 5 (depends on 1) The method of claim 1, wherein the relevance of the job is probability of the member interacting with the job via a user interface provided by the online connection network system. ### Claim 6 (depends on 1) The method of claim 1, wherein the online connection network system maintains a set of job poster profiles, a company from job poster profiles associated with a plurality of jobs posted with the online connection network system and also associated with one or more segment features. ### Claim 7 (depends on 6) The method of claim 6, wherein the one or more segment features include one or more of geographic location, industry, and company size. ### Claim 8 (depends on 6) The method of claim 6 further comprising executing a price adjusting machine learning model with respect to the set of job poster profiles to generate respective price adjusting factors for companies in the set of job poster profiles. ### Claim 9 (depends on 1) The method of claim 1, wherein the price adjusting machine learning model takes as input respective net promoter scores of companies in the set of job poster profiles, respective fees charged to companies in the set of job poster profiles during an investigation period, and respective total values of job applications received by respective companies in the set of job poster profiles during the investigation period. ### Claim 10 (depends on 1) The method of claim 1, wherein the relevance machine learning model takes as input features of the job and features of the member profile. ### Claim 11 A system comprising: one or more processors; and a non-transitory computer readable storage medium comprising instructions that when executed by the one or processors cause the one or more processors to perform operations comprising: accessing a member profile that represents a member in an online connection network and a set of candidate job postings maintained by the online connection network, each job in the set of candidate job postings is an electronic listing of a job opening comprising job features; for jobs from the set of candidate job postings: determining a value of a next application for the job, executing a relevance machine learning model to predict relevance of the job, calculating a rank of the job by multiplying the relevance of the job by the value of the next application for the job; and including a subset of jobs from the set of candidate job postings, based on their respective ranks, into a user interface (UI) for presentation to the member on a display device. ### Claim 12 (depends on 11) The system of claim 11, wherein the determining of the value of the next application for the job comprises: executing an application value machine learning model to predict projected value associated with a job poster of the job; and calculating the value of the next application for the job as the difference between the projected value associated with the job poster of the job and a previously-predicted projected value associated with the job poster of the job. ### Claim 13 (depends on 12) The system of claim 12, comprising using a number of previously occurred applications for the job, features of the job and features of the job poster as input to the application value machine learning model. ### Claim 14 (depends on 12) The system of claim 12, wherein the application value machine learning model is a linear-log mixed model. ### Claim 15 (depends on 11) The system of claim 11, wherein the relevance of the job is probability of the member interacting with the job via a user interface provided by the online connection network system. ### Claim 16 (depends on 11) The system of claim 11, wherein the online connection network system maintains a set of job poster profiles, a company from job poster profiles associated with a plurality of jobs posted with the online connection network system and also associated with one or more segment features. ### Claim 17 (depends on 16) The system of claim 16, wherein the one or more segment features include one or more of geographic location, industry, and company size. ### Claim 18 (depends on 16) The system of claim 16 further comprising executing a price adjusting machine learning model with respect to the set of job poster profiles to generate respective price adjusting factors for companies in the set of job poster profiles. ### Claim 19 (depends on 11) The system of claim 11, wherein the price adjusting machine learning model takes as input respective net promoter scores of companies in the set of job poster profiles, respective fees charged to companies in the set of job poster profiles during an investigation period, and respective total values of job applications received by respective companies in the set of job poster profiles during the investigation period. ### Claim 20 A machine-readable non-transitory storage medium having instruction data executable by a machine to cause the machine to perform operations comprising: accessing a member profile that represents a member in an online connection network and a set of candidate job postings maintained by the online connection network, each job in the set of candidate job postings is an electronic listing of a job opening comprising job features; for jobs from the set of candidate job postings: determining a value of a next application for the job, executing a relevance machine learning model to predict relevance of the job, calculating a rank of the job by multiplying the relevance of the job by the value of the next application for the job; and including a subset of jobs from the set of candidate job postings, based on their respective ranks, into a user interface (UI) for presentation to the member on a display device.