Search optimization based on relevant-parameter selection

A system comprising:

• a memory comprising instructions;
• and
• one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising:
• executing, by one or more processors, a machine-learning program that uses information about users of an online service to train a machine-learning model to calculate parameter preference scores for a plurality of parameters for use in a job search;
• detecting, by the one or more processors, a job search for a user;
• identifying, by the one or more processors, user parameters associated with the user;
• calculating, by the machine-learning model, the parameter preference scores for the user parameters;
• determining, by the one or more processors, search parameters by:
• determining, for each user parameter, a number of job postings with parameters that match the user parameter;
• calculating, for each user parameter, an average parameter relevance by dividing the parameter preference score by the number of job postings matching the user parameter;
• and
• selecting for use as search parameters a predetermined number of user parameters with the highest average parameter relevance;
• performing, by the one or more processors, a search of a job-postings database with the search parameters;
• and
• causing, by the one or more processors, presentation on a display of job postings based on the performed search of the job-postings database.

A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:

• executing, by one or more processors, a machine-learning program that uses information about users of an online service to train a machine-learning model to calculate parameter preference scores for a plurality of parameters for use in a job search;
• detecting, by the one or more processors, a job search for a user;
• identifying, by the one or more processors, user parameters associated with the user;
• calculating, by the machine-learning model, the parameter preference scores for the user parameters;
• determining, by the one or more processors, search parameters by:
• determining, for each user parameter, a number of job postings with parameters that match the user parameter;
• calculating, for each user parameter, an average parameter relevance by dividing the parameter preference score by the number of job postings matching the user parameter;
• and
• selecting for use as search parameters a predetermined number of user parameters with the highest average parameter relevance;
• performing, by the one or more processors, a search of a job-postings database with the search parameters;
• and
• causing, by the one or more processors, presentation on a display of job postings based on the performed search of the job-postings database.