Tuesday, June 10

Citysense and Context That Matters for Search and Recommendation

The big news is Steve Jobs announcement of the new iPhone 2.0 with 3G and GPS support and, more importantly, a big price drop. This means lots more users for the Apple App Store that will soon be opening for users to download and buy software. There will surely be a deluge of applications that leverage the GPS support, such as Loopt, a mobile social network application that will be available for free.

What does this have to do with search? Everything. Location and temporal context is critical to providing relevant information that I need right now. For example, to find alternative movie theatres when I'm at the mall and the movie I wanted to see was sold out.

There has been a lot of talk about context in Information Retrieval, the IRIX 2004 Workshop, CIR 2007, and the work of Susan Dumais using context, among much research. However, much of this has focused on using your documents, browsing history, and e-mail as context. The prevalence of location-enabled mobile phones is beginning to change all that. The upcoming Workshop on Mobile Information Retrieval (MobIR) at SIGIR looks quite interesting.

A new startup that made me think think differently about mobile context, CitySense by Sense Networks. CitySense (see coverage from O'Reilly and GigaOm) is the latest "social navigation and nightlife discovery application" utilizing real-time and historical location data to show "hotspots" in San Franscisco. What's remarkable is the utilization of not just your location, but also the real-time location of people around you. Sense Networks applies machine learning algorithms to analyze the historical information and identify interesting traffic patterns. This could be used to provide location-based popularity to rank local businesses. For example, to recommend the best local ice cream shop that always has long lines, but happens to be quieter right now. CitySense is similar to predictive traffic routing systems such as Microsoft's ClearFlow system [NY Times]. Eric Horvitz also briefly describes ClearFlow in a recent keynote: Machine Learning, Reasoning, and Intelligence in Daily Life: Directions and Challenges.

As Steve Green recently said, "Recommendation is the new search." An ultimate local recommendation system would utilize many factors including the current time, my location from my mobile, weather, what other people like me have done in the situation in the past, and what other people are doing right now.

1 comment:

  1. Sorry I missed this the first time around, but I've got to take issue with "recommendation is the new search"--at least if the idea here is for the system to know me better than I know myself. I'm all for social navigation, but I feel strongly that we need more communication with our IR systems, not less.