who the hell am i to tell me what i like?
Amazon, Pandora, TiVo, iTunes, Last.fm, Google Reader, the list goes on. Personalized recomendation services are a hit and for good reason. There's so much utter rubbish out there to be ploughed through it's just too much for even the most obsessive datarats among us to handle on our own. We need machine assistance to help us sift and sort to find those tasty nuggets we crave so badly.
But the trend has been to assume that we already know what we like and that we should simply have more like it. The much discussed APML is evidence that this line of thinking is still mainstream. The only way to adjust the settings of the suggestion engines in any of these systems is to allow them to profile you as you interact with them. But what if what you're interested in today doesn't directly relate to what you've been paying attention to in the past? How can you still make use of that attention information while actively directing future suggestions?
This sounds like a job for a Real World Scenario!
I'd never heard of bluegrass music until I was nearly done highschool. So I hit the net, scrounged (napster'd at the time) up some MP3's and had a listen. It was terrible. I discovered that I pretty much hate bluegrass. But let's say something like Last.fm was around then and it already had a nice APML like profile of what I had been listening to. If I were able to manipulate my attention profile by editing key concepts as tags and their attention values as sliders I could add a bluegrass tag, slide it up to maybe 50% and let the suggestion engine do it's job.
The end result would be somewhere between a saved search (or smart folder / playlist) and a profiled suggestion. I get all the benefits of cross referencing my recorded tastes with those of the Last.fm masses but with some added direction over where it takes me. Maybe if I'd listened to the kind of bluegrass tunes that other Tea Party, Weezer, and Cranberries fans had been listening to I might have loved it ...
Nah.
Now where'd I really love to see this functionality is in my news reader. Let's say I get it in my head that I want to read more about the 2008 Olympics. I haven't been reading many items in my news reader related to the topic, but if I were looking for feeds on the subject I'd likely be more interested in stories that relate to both the Olympics and Canada, which does come up often in my news reader. Those feeds that cross over could then be suggested first. Guided+Profiled personalized suggestions.
Finally once a facility was in place for something like this, I could see much use in creating "APML bookmarks" for collections of feeds (OPML). In this way I could have all my feeds together, but browse them under say the "Politics", or "Tech" attention profiles as needed. All the while each "bookmark" would grow on its own as it profiled me, but I could also be directing it as I went along.
Do you hear me intertoobs!? I need more control over where I'm spending my paltry attention budget.
But the trend has been to assume that we already know what we like and that we should simply have more like it. The much discussed APML is evidence that this line of thinking is still mainstream. The only way to adjust the settings of the suggestion engines in any of these systems is to allow them to profile you as you interact with them. But what if what you're interested in today doesn't directly relate to what you've been paying attention to in the past? How can you still make use of that attention information while actively directing future suggestions?
This sounds like a job for a Real World Scenario!
I'd never heard of bluegrass music until I was nearly done highschool. So I hit the net, scrounged (napster'd at the time) up some MP3's and had a listen. It was terrible. I discovered that I pretty much hate bluegrass. But let's say something like Last.fm was around then and it already had a nice APML like profile of what I had been listening to. If I were able to manipulate my attention profile by editing key concepts as tags and their attention values as sliders I could add a bluegrass tag, slide it up to maybe 50% and let the suggestion engine do it's job.
The end result would be somewhere between a saved search (or smart folder / playlist) and a profiled suggestion. I get all the benefits of cross referencing my recorded tastes with those of the Last.fm masses but with some added direction over where it takes me. Maybe if I'd listened to the kind of bluegrass tunes that other Tea Party, Weezer, and Cranberries fans had been listening to I might have loved it ...
Nah.
Now where'd I really love to see this functionality is in my news reader. Let's say I get it in my head that I want to read more about the 2008 Olympics. I haven't been reading many items in my news reader related to the topic, but if I were looking for feeds on the subject I'd likely be more interested in stories that relate to both the Olympics and Canada, which does come up often in my news reader. Those feeds that cross over could then be suggested first. Guided+Profiled personalized suggestions.
Finally once a facility was in place for something like this, I could see much use in creating "APML bookmarks" for collections of feeds (OPML). In this way I could have all my feeds together, but browse them under say the "Politics", or "Tech" attention profiles as needed. All the while each "bookmark" would grow on its own as it profiled me, but I could also be directing it as I went along.
Do you hear me intertoobs!? I need more control over where I'm spending my paltry attention budget.