Web 2.0 Asia

CityIn is a new Chinese social network service that "aims to bring people together by matching their personal interests, entertainment, brands, celebrities and others."

So here's how the service seems to work (Reminder: I'm a Chinese illiterate, so my understanding of the service can be very limited. I'm turning more to Angus's English coverage here). In the sample case of iPhone (shown below), there are 11 people who expressed their love of iPhone, so you can browse who also liked iPhone other than yourself; Also shown are other items being liked by those who liked iPhone, such as BMWs.


So CityIn follows textbook ways of connecting people and objects in the so-called "object-centric (as opposed to ego-centric) social networks", which I believe can be summarized:

• Other people who did this include... (e.g. Other people who bookmarked this website are:)
• People who did this also did these... (e.g. People who bought this item also bought:)

But the big question I'd like to throw is, how much of intelligent recommendation technologies are being used for CityIn to come up with those "other people" and "other items" lists? Do they just use simple DB matching to come up with those lists? If that's the case (which I doubt), I wouldn't see much value in the service, because there can be millions of people who love Yao Ming, who each would love all sorts of random, mutually unrelated things.

The so-called "dopplegangers" carry significant meaning only when they share some very unique things with me, not generic stuff like Starbucks. But then, if you found a guy who also liked a '70s album that's known only to two people in the entire world, would you be delighted enough to send a private message to him? I for one wouldn't. (Well, If she's a pretty girl, that's a completely different story of course).

I think the concept of CityIn is quite nice (the best of Lovemarks and Amazon book recommendation, perhaps?), but I'd like to first see how much of personalization technologies the company brings to the table. Because I know that personalized recommendations take either huge amount of data or a very sophisticated, intelligent technology - or actually more likely, both. CityIn might have those - if you know, please shed some light here.