TagWalk Blog

Foursquare and Locations

After many months of operation, TagWalk has collected over 100 million tweets, and tracked over 40 million URLs. There are increasing numbers of Foursquare URLs popping up, and due to good RESTful design from the Foursquare team, I noticed that when users tweet their checkins, it is possible to pattern match their venue URLs.

In true mashup fasion, after a quick nosey through the Foursquare API documention, I was quickly able to fetch additional meta data about venues and present them nicely in the links section. In the same way image thumbnails are shown TwitPic and the other twitter photo services.

I ♥ Recommendation Engines

I am a massive fan of recommendation engines. One of my goals for building TagWalk was to create a recommendation engine that would suggest users with the most reputation for particular subjects or #hashtags, and show popular links being tweeted by users. My inspiration is probably the oldest and most successful example I can think of, Amazon’s “Customer who bought this also bought…”

Location-based Recommendations

Treating Foursquare URLs as symbolic locations instead of web-pages unlocks lots of new meaning burried in Twitter data. It makes location based analytics and recommendations possible. By crunching some historic data it was possible to create a very crude location recommendation engine, or rather, “people who went here, also went there”

Some locations of interest and their related “people who went here, also went there” recommendations are: San Francisco Airport (SFO) ✈, Twitter HQ, Foursquare HQ, Facebook HQ, Tech Crunch HQ , Y Combinator, The White House, and the San Francisco Apple Store.

Sample Size

Foursquare recently trumpeted their 100,000,000th checkin. This dataset covers less than 0.2% of this; just over 150,000 checkins and approximately 80,000 locations. This is a large enough sample size to see some interesting relationships start to emerge. As the population of Twitter users that TagWalk follows has a statistical bias to San Francisco and other tech areas, it figures that these tech-savvy areas have better coverage by my data set than others areas.

What next?

Whilst the data set is not (and can never be) complete, a large enough sample size can yeild “good enough” results. By selectively loading more data, the data set can be adapted to improve the quality of results. Over time, TagWalk will continue to collect small volumes of checkins, but specific search filtering could by used to prioritise certain regions or businesses.

With further digging around in the datamine, it is possible to produce location-based recommendations, or rather, “people who went here, also went there” and for more considerable computation effort, “people who went here, also have the following interests, reputation, talk with these users, and visit these web sites.”

If you run a business, particularly with many locations and would like to work with me to get to know your customers better, then please get in touch.

You can follow me here: @timhastings