Netflix Prize has lessons for local search

The Netflix Prize seeks to substantially improve the accuracy of predictions about how much someone is going to love a movie based on their movie preferences.

So what does this have to do with local search? Researchers working on this problem have found that you should ignore everything you know about the movies (the genres, the actors, etc.) and base your predictions on how people have rated them.  For local search this means we should base recommendations on people’s preferences – the businesses they like and have used – rather than categorical data about businesses.

Lessons from the Netflix Prize

From a New York Times article on the Netflix Prize:

“You can find things like ‘People who like action movies, but only if there’s a lot of explosions, and not if there’s a lot of blood. And maybe they don’t like profanity,’ ” Volinsky told me when we spoke recently. “Or it’s like ‘I like action movies, but not if they have Keanu Reeves and not if there’s a bus involved.’ ”

So, you can’t base movie recommendations on a simple categorization like ‘action movie’.  Its difficult for most people to articulate what they like or dislike about a movie. So, its better to base predictions on what you know about which movies the person likes or dislikes. In fact, researchers have consistently found that adding in categorical information about the movies doesn’t help with making predictions at all. (And there have been numerous attempts.)

Applying it to Local

The research from the Netflix Prize shows us that you shouldn’t recommend a pizza place to someone just because it’s a pizzeria (i.e. it’s category) and it’s ‘close enough’ to you.  That’s because the best choice can be influenced by many factors:

  • Am I a person who likes my pizza ’straight-up’ or exotic?
  • Am I in a rush and looking for the quickest option?
  • Am I bored and looking for something new?
  • Have I just come back from an exotic location and looking for an old favorite?

Today,  local search engines continue to rank results based primarily on  factors like category and location.  Even sites such as Yelp with extensive reviews rank and present results to users without considering the preferences of the searcher.  This is akin to simply presenting movies ranked by popularity alone.

Research coming out of the Netflix Prize shows the way towards a different way of thinking about local.


Forget search: local is a recommendation problem


2 Responses to “Netflix Prize has lessons for local search”

  1. 1 Neil Hepburn May 7, 2009 at 1:13 pm

    Those are some powerful insights indeed!

    I think it would be neat to have a similar competition for Local Search (obviously this would be a bit trickier to do for privacy reasons), and see what patterns emerge.

  1. 1 Thinking holistically about local search « Predicting What Consumers Want to Buy Trackback on May 9, 2009 at 11:49 pm

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