Smart Phone Tells You What To Do

Articles Written Between Sept and Oct 2007
Dai Nippon Printing (DNP) and Palo Alto Research Center Inc (PARC) have been collaborating since 2005 to develop a context and activity aware system that recommends information about "local area" activities, such as shopping and dining, movies and bookstores or concerts and bars, matching the consumer's location, time of day and personal tastes. PARC named the software Magitti (derived from two early design concepts: a magic scope and a digital graffiti system). When this software is installed on your GPS-enabled phone Magitti starts to suggest what to do in your area. When an individual accesses their phone they will instantly see a list of recommendations. If it's noon, the osftware might suggest local restaurants. If it's 3PM, it might recommend a nearby boutique for shopping. If it's 9PM, a list of pubs might appear. Of particular interest to mobile recommendation, is its usefulness in unfamiliar areas to the user. For tourists, vacationers, business travel or even exploring unchartered areas of your own city, mobile recommendations prevent users from having to wander foreign streets and ask someone passing by for directions or their opinion, which has the potential to be a nightmare!

Interestingly, "Magitti pulls GPS data from a user's phone, as well as text messages, emails and information about events saved in the phone's calendar, and uploads it to a server, along with the user's search terms", says Kurt Partridge (a researcher at PARC). He states text messages are important bits of information because they often include data about future plans. If for instance a person gets a text message suggesting sushi, the software will put recommendations for sushi and Japanese restaurants higher on the list. The mere mention of text messages, emails and calendar notes being monitored and stored have raised several privacy concerns. PARC states these messages are only kept for a short amount of time but ultimately, there's a trade-off between privacy and convenience. Bo Begole, a co-leader on the project, remarked that the analysis happens on the handset and not on the servers at the company.

The software uses artificial-intelligence algorithms to make tailored recommendations. After reading several articles, I would infer this recommender system uses a hybrid model (context, demographic and collaborative). By making recommendations from text messages, emails and calendar notes and comparing them with location, time of day, and other personal tastes it seems to rely on context and demographic. I also read that collaborative filtering is used to recommend things that others with similar tastes like and allows people to input their own ratings and reviews.

Testers have noted the software works more often than not. One tester detailed at 11:30 am (pacific time) the system offered nearby lunch restaurants, a home furnishings store and a gym, noting it was rather easy to expand or limit the distance of suggestions and the type of cuisine. They likened the application to the Apple iPhone but commented the interface isn't nearly as slick. While the software is functional, there are still technical problems that have not been solved such as category ambiguity. Shopping could mean farmer's market or Macy's. Eating could mean sitting down at a restaurant or picking up a sandwich at the grocery store or enjoying a meal at home so the semantics of keywords still need to be analyzed for each individual in an effort to provide more accurate results.

Magitti will go through public trials with young adults in Tokyo, Spring 2008. Deployment is scheduled next year in Japan but it is unlikely this software will be sold in Europe or in the U.S.

DNP, PARC Jointly Develop Recommender System for Mobile Terminals
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Snapshots of Application