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Tags, Collaborative Filtering, and GROOP.US

by David Sturtz

Rashmi Sinha recently posted, Collaborative Filtering strikes back (this time with tags), discussing the intersection of collaborative filtering (recommendation systems like those on Amazon or Netflix) and tagging.

“One of the roadblocks to collaborative filtering is user input, some expression of interest by a user that you can hook into. Tags provide such a hook. On the other hand, tags desperately need good ways of supporting findability. As I argued before, you can go only so far with lists. Which is why we are seeing interests in clusters, facets and collaborative filtering. Additionally, both tags and collaborative filtering provide inroads into the Long Tail.”

Investigating this idea further, I ended up rediscovering GROOP.US. This site has been noted mainly as an interesting visualization of tagging data, which it is at first glance. But, the goals of the project are to unveil and combine the individual tag clusters and the social networks within in order to help users explore new tag clusters and to produce recommendations. The visualizations are just one way of presenting this much more useful end product.

There is a ton of information and analysis being displayed in these visualizations, probably too much for the average user. Solution Watch has a good explanation of what is going on in the GROOP.US demos. It will be interesting to see how this project progresses, and if it develops into a useful system. For now, a thorough explanation of the theory and methods of the GROOP.US project is available in their thesis: Groups in Social Software: Utilizing Tagging to Integrate Individual Contexts for Social Navigation (Bielenberg & Zacher, 2005) or much more briefly in this summary.

As a side note, John Udell seems to have developed a sort of collaborative filter for (update here). But I can’t seem to uncover any more about his methods or results.

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