From the post:
“…you’d subscribe to an OPML document which contains a list of feeds that someone is reading, some organization is recommending, or some service has generated (such as “Top 100″ list). Changes to the source OPML document would be synchronized, so that you’re automatically subscribed to feeds added to the reading list. Likewise, you’d be unsubscribed from feeds removed from the original OPML.”
So what are the implications:
- What if you like the feed, and you don’t want it dropped?
(There could be a notification that this is happenning, and you could override it, not accepting a deletion or addition)
- What if you have flagged items from a feed that is being dropped?
- Tags for each feed, so you could subscribe to an OPML file of all feeds with a given tag
(if an RSS Reader already has tags for feeds, and each tag has an OPML file, then this is already implemented.
- De-duping items from feeds in an OPML file that you are already subscribed to
(Since you can file a feed in multiple tags in Rojo, when you read a feed in one tag, the appearance of the same feed in another tag will be automatically be marked as read
…so it doesn’t matter that the feed appears twice, you won’t have to read the same content twice, therefore if the feed already exists in another OPML reading list, or as one of your single feed subscriptions, it doesn’t need to be filtered out if it appears in a new OPML reading list or file that you are importing).
- Ranking/Rating statistics included for each feed within an OPML file
From the post:
“…when you export OPML from one aggregator, the aggregator you import into would know which feeds you’re paying the most attention to. This could be used for any number of things - recommending related feeds, giving higher ranked feeds higher priority in feed listings, etc.”
Each RSS reader collects personalised statistics differently and in a number of ways:
eg. How often you read a feed (what about feeds that don’t post often, but are one of your favourites), which items you click on (although you may click on something only to find out you don’t like it), how often you flag or save items from a feed (what if you save in another bookmark manager, how would it know this?)
Ranking on how many people have read that feed is based on popularity…PersonalBee views popularity in an inverse way, and overcomes overload by throwing up a list of fresh keywords grouping new items.
Ranking on how many time you have read that feed is based on relevance, this also applies within a social circle…see more at Findory…searchfox does much the same.
Bloglines uses Chameleon to rank most often read feeds
- Ranking by rating/voting for each item within a feed
As Alex’s post articulates, you may click to a post only to find you’re not interested, so this is ambiguous, whereas a manual vote to a post is an explicit interest - just like when people link to blog posts…or even the amount of time you spend reading a post
…although, ratings need people to spend time making a decision on each post they read, even though this is extra effort it will pay off in the end, if the relevance of the posts in your RSS reader are ranked according to your social group voting each post, you will all enjoy the reward of cutting your reading time to premium stuff.
Even though we know the benefits will be great the obstacle is the explicit or manual process or voting, what are the other options.
Instead of manually ranking, the relevance of each post could rank higher in your RSS reader if it has been bookmarked in del.icio.us or a similar service, but then this wouldn’t work as new content is “new” so it isn’t bookmarked yet…but this isn’t too far from flagging or saving items in an RSS Reader.
So anyway the idea is to store attention data such as voting or rating within the OPML file…so compared to feed ranking which is based in implicit reading behaviour, item ranking is based on explicit vote casting.
…more on this follow up post, includes a great table with a summary of possible OPML attributes, such as, feed ranking, and item rating.
So what are our reading needs (attention):
Personalisation (ranking/relevance)…this is what we have spoken about above
Recommendation…we have spoken about this above, such as feed ranking via reading behaviour, and recommended items via voting, also recommended items via reading behaviour.
This is also achieved by incoming links eg. Technorati (authority, but this is more popularity, not personal enough), another way is finding related news eg. Google News (or is this simply popularity, although I like the way it collapses similar items), although Memeorandum is machine based it only inludes incoming links that are substantial, so it has less noise.
UltraGleeper, from the website:
“The Ultra Gleeper takes your weblog subscription list and starts from there. It crawls the web for things you haven’t seen and shows you the pages it thinks you’ll like. Your feedback improves its ability to give accurate ratings. With the Ultra Gleeper can find new pages and new weblogs to read. And if you have your own weblog or use del.icio.us, the links you post there will be automatically turned into ratings.
The Ultra Gleeper solves or avoids the problems that give recommendation engines a bad reputation. It won’t give you a lot of links you’ve already seen, because it knows about your subscriptions and what they’ve posted. It won’t just recap the most popular links of the day, because its indie rock algorithm distrusts excessive popularity. It won’t ask you for a lot of calibration ratings up front: you already gave those ratings by telling it what you subscribe to and pointing it to your weblog and/or bookmark page.”
I haven’t played with Sphere yet, but I’m finding it hard to get my head around TailRank, is there somewhere I can find an ultra informative blog post on using TailRank…there is some postive talk on viewing posts in the blogosphere, a social group, and your attention file…which makes it more personal than Memeorandum, unless Memeorandom decides to let people import OPML files (would this then be like TailRank?)
I would like to limit Memeorandum to my OPML file, so my OPML file is a mini blogosphere and it will thread/cluster items only from within my OPML file…and I could even include other OPML files as a source for related or linked items (this way I’m choosing the blogs from the blogosphere as the incoming links, I don’t want to see incoming links from blogs I don’t care about…although this limits discovery of new voices).
Or my OPML file is a mini blogosphere and it will thread/cluster items from within my OPML file and the rest of the blogosphere…this version is like Memeorandom as it exists accept the items are only those from my OPML file, but the threads are from the whole blogosphere.