Introducing a faster place search
Jon Parise | Pinterest engineer, Discovery
We launched Place Pins a little over six months ago, and in that time we’ve been gathering feedback from Pinners and making product updates along the way, such as adding thumbnails of the place image on maps and the ability to filter searches by Place Boards. The newest feature is a faster, smarter search for Web and iOS that makes it easier to add a Place Pin to the map.
There are now more than one billion travel Pins on Pinterest, more than 300 unique countries and territories are represented in the system, and more than four million Place Boards have been created by Pinners.
Here’s the story of how the Place Pins team built the latest search update.
Supercharging place search
People have been mapping Pins for all types of travel plans, such as trips to Australia, places to watch the World Cup, cycling trips, a European motorcycle adventure, best running spots, and local guides and daycations.
Even with the growth in usage of Place Pins, we knew we needed to make the place search experience more intuitive. In the beginning, the place search interface was based on two distinct inputs: one for the place’s name (the “what”) and another for the search’s geospatial constraint (the “where”). We supported searching within a named city, within the bounds of the current map view, and globally around the world. While powerful, many Pinners found this interface to be non-intuitive. Our research showed Pinners were often providing both the “what” and the “where” in the first input box, just like they do when using our site-wide search interface. With that in mind, we set out to build a more natural place search interface based on just a single text input field.
The result is our one-box place search interface:
We start by attempting to identify any geographic names found within the query string. This step is powered by Twofishes, an open source geocoder written by our friends at Foursquare. Twofishes tokenizes the query string and uses a Geonames -based index to identify named geographic features. These interpretations are ranked based on properties such as geographic bounds, population, and overall data quality.
This process breaks down the original query string into two parts: one that defines the “what”, and one that defines the “where”. It also lets us discard any extraneous connector words like “in” and “near”. For example, given the query string “city hall in san francisco”, the top-ranked interpretation would return “city hall” as the “what” and “san francisco” as the “where” while completely dropping the connector word “in”.
Some geographic names are ambiguous, in which case Twofishes returns multiple possible interpretations. By default, we use the top-ranked result, but we also provide a user interface affordance that allows Pinners to easily switch between the alternatives.
Configuring place search
We use the result of the query splitting pass to configure our place search. Foursquare is our primary place data provider, and Foursquare venue search requests can be parameterized to search globally or within a set of geospatial constraints.
A single query can produce multiple venue search requests. Continuing with our example, we would issue one search for “city hall” within the bounds of “san francisco” and as well as a global search for the entire original query string “city hall san francisco”. This approach helps us find places that have geographic names in their place names, like “Boston Market” and “Pizza Chicago”.
We experimented with performing a third search for the full query string within the bounds of the geographic feature (“city hall san francisco” near “san francisco”), but in practice that didn’t yield significantly different results from those returned by the other two searches.
If we don’t identify a geographic feature (e.g. “the white house”), we only issue the global search request.
Blending and ranking results
We gather the results of those multiple search requests and blend them into a single ranked list. This is an important step because Pinners will judge the quality of our place search results based on what’s included in this list and whether their intended place appears near the top. Our current approach takes the top three “global” results, adds the top seven unique “local” results, and then promotes some items closer to the top (based on attributes like venue categorization).
More to come
In early tests, the new one-box Place search interface has been well-received by Pinners, and Place Pin creation is higher than ever. The updated place search is now available in the Pinterest iOS app and our web site, and look for it to make its appearance in our Android app soon.
One-box place search was built by engineers Jon Parise, Connor Montgomery (web) and Yash Nelapati (iOS), and Product Designer Rob Mason, with Product Manager Michael Yamartino.
If you’re interested in working on search and discovery projects like this, join us!
Jon Parise is an engineer at Pinterest.