twittteruser recommendation system based on algorithms We've found that the power of suggestion can be a great thing to help people get started, but it's important that we suggest things relevant to them. We've created a number of algorithms to identify users across a variety of clusters who tweet actively and are engaged with their audiences. These new algorithms help us group these active users into lists of users by interests. Rather than suggesting a random set of 20 users for a new user to follow, now we let users browse into the areas they are interested in and choose who they want to follow from these lists. These lists will be refreshed frequently as the algorithms identify new users who should be suggested in these lists and some that are not as engaging to new users will be removed
Kathrin Passig on filter/recommendation systems und individualisierung/personalisierung + microcontentisierung und neue verkonglomatisierung + zersplitterung von "person" im netz (gute tweeps may be schlechte blogger, flickrees etc.) +++ (das glaser zitat ist von hier: http://www.stuttgarter-zeitung.de/stz/page/2173605_0_8264_-die-leichtigkeit-der-digitalen-materie-kultur-zerstreut-sich-im-digitalen.html , reaktion u.a. hier: http://faz-community.faz.net/blogs/crtl-verlust/archive/2010/05/04/links-zum-kontrollverlust-ii.aspx)
hackr anlässlich twitter/buzz-follow-reccomendations über grundweg foaf vs. content bases &infoökonom. dilemma redundanz vs. serendipity (in etwa, müsste man bei zeiten besser zsfs)