Learning Hiring Preferences: The AI Behind LinkedIn Jobs

The technique we leverage to train the targeting to get smarter is called “online learning,” which is learning that happens in real time as our members use the product. Based on how you interact with candidates, our algorithm learns your preferences and delivers increasingly relevant candidates across the Jobs product. If you’re consistently interested in candidates who are, say, accountants with leadership skills, or project managers who are adept at social media, we’ll send you more of those. And this all happens online in real time so that your feedback is taken instantly into account.

LinkedIn provides multiple avenues for finding success in filling your job posting. Our matching technology shows up on both the job-seeker and company side throughout our Jobs product. One of the challenges we face in building our products is that we’d like for the relevance aspects to be unified across these different channels so that it feels like a cohesive experience. For example, when you use job targeting to specify the skills or years of experience you’re looking for, we are able to use that information to determine which candidates you should reach out to, highlight your job to the members that may be most interested in and qualified for it, and place the most qualified applicants at the top of your review list. The online learning component helps power all of those product features.

This online learning-powered recommendation system uses signals such as the job description, candidates you’ve reached out to or archived, and members interested in jobs like yours to match the most suitable candidates to your open role. Interactions with an applicant versus interactions with a recommended match may be fundamentally different, and so we explicitly represent the channel in which the candidate was discovered in our machine learning model. This also gives us the ability to incorporate online learning from user feedback across additional channels outside our Jobs product in the future like Recruiter Search.

Convincing someone to consider your company and role is harder when they’re not actively seeking a job. One of the engineering challenges here is that we need to optimize for two-way interest. We want candidates to find the messages they receive compelling and not miss out on any opportunities, and we want hirers to reach out to candidates who are qualified and might be interested. As a result, in passive sourcing channels like Recommended Matches, we utilize a member’s Open Candidate status and other indicators of job seeking intent to present those members higher in the ordering. Our algorithms present the candidates most likely to accept your outreach based upon whether they are qualified, have displayed job seeking intent, and would be interested in your job.

Deep dive into online machine learning

Feedback about candidates is aggregated in real time and associated with the corresponding hiring project. A hiring project represents an opening to be filled, and may have a job post, search queries, candidate feedback, and other useful details associated with it. The aggregated information is utilized to produce features in our machine learning model. This aggregation is done at the hiring project-level so that we can learn a customer’s preferences for the specific opening.

For each hiring project, we want to learn which profiles attributes (e.g., skill, title, industry, etc.) might be most relevant based upon the feedback for each candidate with those attributes. We refer to these profile attributes as “term types.” Each feedback type (e.g., Message, Archive, Skip, etc.) from each candidate channel (e.g., Recommended Matches, Job Applicants, Recruiter Search, etc.) could mean something slightly different, so we create Personalization Features for each channel, feedback type, term type combination.

For those interested in more detail, we provide the actual equations we use below. Let S be the set of all channels, R be the set of all feedback types (such that the set of all actions would be the cartesian product of S and R), and T be the set of term types. The weight of a term i of type t from rating r in sourcing channel s is determined by the following equation:

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