Have not posted in a while - variety of computing and personal issues in the way.
Sitting here reading news on the aggregator, and I came across this blog entry - It reminded me of the way that I've started to use Goldmine software's Org chart function. This function is an interesting one where you can assign people to groups but unlike straightforward group assignment, as you search through a few people, the groups they are members of accumulate in the org char list.
Implicit Preference Maps
Now what does this have to do with preference mapping? My intuition linked them immediately as I read this, and I guess it depends on whether your groups/orgs are affiliation-based groupings connected to interests or to companies or schools, etc. When you think consciously about how you receive your information and who you turn to for expert advice, these real-world connections in your personal social network help to shape your implicit preference map.
Meta-Preference Maps
In other words, we're back to the bow tie map of amazon books. If you are deep in one side of the bow tie, then you're preference map will be biased very differently from the other side of the tie, or the connecting knot in the middle! This suggests Meta-Preference Maps.
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Whose personality do you want today?. l.m.orchard commented regarding the using Bayesian analysis on news. In fact, as soon as I saw it I remembered, I had read his piece already. It was probably his writing that triggered my initial interest in using a Bayesian classifier in K-Collector.
Re-reading that piece I got an interesting different angle since his approach was to blend a Bayesian classifier with his news aggregator to try and have it prioritize news he would find interesting and not to categorize it by topic. I think this is a much more scalable task, from a K-Collector perspective, than what Jon is experimenting with. I think the efforts of training a system-wide recognizer to differentiate between topics would be too much for most users of the product to bear.
Our product roadmap for K-Collector already includes allowing users to personalize the system. For example we think that people should be able to say which feeds they think are relevant on different topics. Notice that this is a much very granular relationship since it means that I can say "Matt Mower is a real expert on the topic sock puppets" but that this says nothing about how relevant I am on "dating." or any other topic. Indeed each user might rate the exact same sources differently over a wide range of topics.
What might be interesting is if people could "share" and "subscribe to" preference maps. As a new user of the system you might not really know who is relevant on any particular topic. But imagine you worked with David Weinberger, Phil Wolff, or Dan Gillmor. If you knew them and trusted their judgement you could pick one of their preference maps as a starting point and immediately gain a usseful insight into the data as it is structured by topic. You might even switch between personalities to get more perspective!
Thanks to l.m.'s piece I am now wondering also about whether a Bayesian classifier might be more use in helping users to establish their own preference maps about which content is most relevant to them.
[Curiouser and curiouser!]
7:06:23 AM 
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