Personalized Job Feeds and Machine Learning
Personalized Job Feeds and Machine Learning
Learn how to leverage personalization technology and machine learning to improve connections between job seekers and jobs. The technology is complex, but it comes down to 4 steps.
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Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Read how Alegion's Chief Data Scientist discusses the source of most headlines about AI failures here.
Product managers for job sites face two fundamental problems. First, the top candidates are not actively looking for a job, making them difficult to seek out and find. Second, the top jobs are quickly filled and typically attract in-network candidates. So, while you have top candidates and great jobs on your site, it can be hard to connect the two at the right time.
Throughout this article, we'll explain how to leverage personalization technology pioneered by social networks to improve the connection between job seekers and jobs. While the technology is complex, it basically comes down to four steps.
Step 1: Track Explicit User Preferences
As a first step, you'll want to store all the explicit user preferences. This includes things like location, industry, seniority level, pay range, and job titles. For instance, let's say that a user searches for programming jobs in Boulder with a salary above $100k. After the user has entered this search parameter, you can give them the ability to follow or save the search. While this is a good starting point for creating personalized job feeds, setting up filters is a lot of work and most of your userbase won't take the time to configure them. This is why tracking implicit user intent (or Step 2) is so important.
Step 2: Track Implicit User Intent
There are many "events" on your job site that provide good insights into a user's preferences. Common examples include:
- Clicking on a job listing
- Saving a job
- Opening an employer profile
- Applying for a job
- Clicking on a job in an email
Each of these events signal intent and provide clues about what a given user is interested in. Over time, you will learn that a given user often looks at open jobs at Apple, or maybe he/she is looking for jobs focused on a certain programming language. These small events build up and allow you to create a profile of a given user's interests.
Step 3: Leverage the Network
Another way to deliver the most relevant jobs is to leverage the rest of the user base to improve job recommendations. Find users with similar criteria and see what jobs they're viewing and applying to. There are a lot of correlations that can be used based on user job history (titles, companies, locations) as well as user behavior.
Step 4: The Feedback Loop
Combining these three techniques will create a strong feedback loop. Every time a user visits your app, you learn more about their interests; thus, your job recommendations improve. Even passive job seekers continuously get better recommendations if they even occasionally open your digest email. These tailored recommendations encourage people to come back to your site, further improving the quality of your recommendations. As you can imagine, this feedback loop greatly improves user engagement.
Increasing User Engagement Using Personalized Job Recommendations and Recommender Systems
Presenting the job recommendations to job seekers can be done in several ways. The obvious is a single feed shown on your app's homepage. An alternative approach is a weekly email digest with recommended jobs.
Most product owners are continually looking for ways to increase user engagement, time spent in the app and methods for re-engagement. An email with interesting and relevant jobs not only reminds job seekers to return to the app but can also keep passive seekers interested. Engagement (opens, clicks, etc.) with the email is also tracked and fed into the engine, refining the results even further.
The Power of Machine Learning and Feeds
This engine I spoke of leverages machine learning, which enables you to build, train, and tune a model that is designed to predict individual user interests. This type of technology was pioneered by social networks such as YouTube, Instagram, and Facebook. Two great examples of machine learning in action are Instagram's discovery feed and Quora's digest email. Every time you engage with Quora or Instagram the recommendations become a little bit more targeted.
Imagine a list of jobs presented to users that continue to get more and more relevant — piecing together things that piqued their interest without them even knowing it. Suddenly, the perfect job bubbles to the top, combining everything they're looking for, giving them room to grow but drawing on the best parts of their experience to make them the ideal candidate. Personalization gives job sites a great opportunity to build up a competitive advantage by better understanding a user's interests.
Published at DZone with permission of Balazs Horanyi , DZone MVB. See the original article here.
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