We've revamped the Globus Recommender to make it much smarter in using locations. Now, even if addresses, country, postal codes or postal locations are spelled differently or vary slightly, we can handle it thanks to the integration with Google Maps API and the improvements made in Globus.
A major new feature in the recommender is calculating the distance from a candidate's home to their potential workplace. This allows us to favor candidates who live closer, enhancing their chances of being matched with suitable orders. Plus, you'll see the distance in kilometers right in the candidate list, making it clear how far a customer is from the candidate's home adress.
We’ve also introduced a new way of matching candidates based on a "waterfall" logic, which considers both their home location and their preferred work locations, ensuring a more thoughtful and efficient matching process. We will now efficiently match a customer-specific address tied to an order with a candidate's preferred location specified on country, county, municipality or city level.
Both matching rules (home location and preferred location) can be separately configured.
Improved location filters
In addition to improvements in the recommender, we have also made several improvements to the filtering on locations. It is now possible to filter both candidates and orders by area and distance/radius utilizing the same waterfall logic. There are now two filters available on candidates, home location (where they live) and preferred location (where they want to work). These filters work a bit differently.
Home location: When using the Distance or Area filter for home location, all candidates with an address within the selected area or radius will show up in the list. It is possible to add the same filter twice, one to include and one to exclude for more detailed filtration. Example "show me all candidates living within 100 km of the Stockholm Central station, but exclude those that live in Huddinge Kommune". You can then first add the "positive" filter who you want to include:
Then add the same filter again and choose "none of" for the areas to exclude:
Using this method it is possible to filter on most scenarios.
Preferred location filters candidates on where they want to work. This data is fetched from the ATS system. Below is an example of three candidates with different preferred locations in Recman and how filtering on preferred locations on different levels will work. It is also possible to add the same rule again to exclude more detailed locations, similar to the example above.
