Location is an important decision-making factor for many end users. Hotel aggregators, job search portals, property listing companies all filter out results that are too far away. If the results page shows locations that are hard to reach, conversion rates will plummet.

If you’re quality scoring results based on straight-line distance, you’re not personalising your results page as well as you could be. That’s because we never truly travel in a straight line, instead we’re at the mercy of the transport networks around us. Distance never considers the context of accessibility, which is unique to every location around the world.

Using distance is impacting search result ranking because:
1. It doesn’t acknowledge that long distances in quiet rural areas are easier to travel vs. congested urban areas
2. It ignores that some locations are situated on fast transport routes – they could appear far away but they may be really easy to access depending on the local infrastructure
3. Local geography can massively impact accessibility – mountains, rivers and beaches all provide accessibility challenges

The solution:
Using real world examples I’ll discuss how to integrate travel times into your recommendation model and what the effects are for businesses and end users. I’ll also discuss how the presence of transport data on search result listings helps reduce cognitive load when users are making a decision.

I’ll end with a quick demo showing how to build it into your recommendation engine.

The Search track is presented by OpenSource Connections