To many new users entering the kuro5hin community, things can seem a bit intimidating at first. Many new users do not really realize some of the classic content that has been created for k5. The problem, of course, is that these people merely look at what is on the front page, and maybe what is on the section pages.
There have been some attempts to create a kind of community-edited guide to K5. One such attempt is Ko4ting, a wiki that, amongst other things, contains a k5 hall of fame.
This hall of fame lists what users think of as some of the best classic stories that have featured on k5. However, I was thinking, people have different tastes, so a story which one person likes is not necessarily a story that others like too.
I feel the best way to help users find content they like on k5 is through the use of a collaborative filtering based recommender system.
It is probably a good idea to explain what these terms mean. A recommender system is basically a system that can be used to recommend content to users. The user specifies some content that they like, and some content that they dislike. This information is then used to find content which is likely to appeal to the user. The collaborative filtering approach to recommending basically means that if a thousand users who liked story A also liked story B, then it is probably a good idea to recommend story B to fans of story A.
If you want to see a collaborative filtering recommender system in action, I suggest you go have a look at Amazon.com. Type in the name of your favourite CD, and it will suggest other CDs to you. I find that the recommendations it makes are quite good usually (it certainly helps me when I'm trying to find new music to download :P). For example, when I asked amazon about music similar to Aphex Twin, the list produced certainly contained some new stuff I did not know about and certainly enjoy.
I think that such a system could be implemented for K5 stories relatively easily. Users would be asked to specify some stories that they liked, and some they disliked. This information required here is rather like the ratings used in the story queue. Of course, the system works better when the user provides more detailed information regarding their interests, so ideally the system would be integrated into K5 and would allow users to specify their vote on every story.
This information is then stored in the user profile. When a user asks for recommendations from the system, the system then compares different user profiles to find new stories that might be of interest to this user. Another possibility is that a user requests stories "like" the current story.
One thing to note is that stories that are liked by a large part of the user population are more likely to be recommended to other users, so there is an automatic floating to the surface of the better stories.
Enabling it to do content-based recommending too could further enhance this system. Collaborative filtering systems don't usually look at what the actual content is. But by looking at the words in a story, filtering away all the most common words in English, one can get a grasp of what this story is about. However, this would be a nice addition for later. Collaborative filtering should be a good thing to start with.
Some people might argue that these kinds of automated systems are not a good way to recommend content to users, and the best filters for quality are, in fact, other humans. However, I feel that these two ideas do not have to be mutually exclusive. For example, Amazon.com uses both collaborative filtering user lists, with it's Listmania system. Humans make these lists, and then collaborative filtering is used to suggest lists that contain content users have said they like.
I'm wondering what the k5 community thinks of this idea. I personally think that this system could help preserve some of the older content on k5, which doesn't happen very well currently.