I am at TagCamp right now. There are a great set of participants and sessions. Just finished my sessions on Findability with Tags where I talked about Clusters, Facets and Collaborative Filtering. Scott Golder (from HP Labs) who wrote a great paper on the structure of collaborative tagging systems took notes for the session. Here they are.
I am headed to TagCamp later today. Its in Palo Alto, so there is not that much heading to do. I was atBarCamp as well, and like these self-organizing type of events. The participant list for TagCamp looks great. There are so many things to talk about that I am having a hard time deciding among them. Here are the topics I am considering.
- Continue with analysis of tagging/categorization from a cognitive perspective and its implications
- Tagging and the Practice of Information Architecture: How tagging is complementary to current practices in Information Architecture. How can both work together?
- Better findability with tags: Clusters, Facets and Collaborative Filtering
- Session focused on Collaborative Filtering and Tagging
- Will tagging scale? Will it move beyond early adoptors? How to make that happen
These are the topics I am considering and unable to decide between. What sounds the most interesting? Feedback welcome.
On another note, just in case you want to know more about what I think about blogs and blogging, read this interview by Rebecca Blood. Her insightful questions forced me to think about why I blog (and face upto the fact that I really wanted to be a writer!).
A long time ago, in a galaxy far away, I became interested in collaborative filtering. Well, it was five years ago, and I was at UC Berkeley, but it seems like eons ago.
What is collaborative filtering? Technically, it’s an algorithm for matching people with similar interests for the purpose of making recommendations. In non-technical terms, it’s a system for helping people find relevant content. Unlike search, where you parse a query to and the most relevant content, with collaborative filtering you find some way of gauging an individual’s interest in content, and then recommend what other similar users liked.