Thinking holistically about local search

Emerging mobile and social applications are changing the way we find local information from a search paradigm to a recommendation paradigm.  Just this week we saw the announcement of several new products promoting this shift – which Greg Sterling reflects on in this post.   And I agree with Greg that in some ways we have almost come full circle:

The underlying consumer behavior is simply asking for word of mouth recommendations and is as “old as the hills.” But the ability to efficiently ask many people for advice or a local business referral at once online is new. Reviews were step one; the combination of quasi-real time answers and social networks is an evolution of that phenomenon.

We’re seeing many different approaches to capturing and sharing opinions — and people vigorously debating the merits of these approaches.  Is it better to have lengthy, insightful reviews or should you just have a simple rating or voting system so you get more participation?  Can you just ask your friends?  Is an answer format better than a review format?

It’s going to be great to see how it all evolves – exciting times!

I believe a holistic and inclusive approach will be needed.  Perhaps the greatest challenge in local information is to achieve sufficient depth and breadth to provide truly meaningful recommendations at the local level.  A modest sized city has tens of thousands of businesses.  This means you need millions of points of view in order to fairly represent the different needs and preferences of consumers.  Simply put:  you need active participation from a large population of local users.

This has several practical implications:

  1. You need to accommodate the different ways users want to interact with local information, but still be able to aggregate this information in meaningful ways.
  2. We can’t afford to ignore the implicit signals provided by all users.  These signals include the things they search for, the maps they request and the businesses they call.  Research on movie recommendations published by participants in the Netflix Prize clearly shows that this kind of implicit data is critical to creating high quality recommendations.
  3. A small percentage of participants will create the majority of the explicit opinions – the silent majority still needs a way to find and evaluate opinions that are consistent with their preferences.  We won’t all have 1000+ friends to ask.


Netflix Prize has lessons for local search.
Forget search: local is a recommendation problem.


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