09
Jul

Re-post from DiggMatch

Digg released its recommended user/story algorithm last week - this algorithm had taken them over a year and a half to develop and I must say, I am very, very disappointed. As a social news website first, and a social networking website second, the focus of digg should always be on the stories. Sure, it is nice to find users that share my digging tastes, just like it is nice to find people to share my listening tastes over at last.fm, but I do not go to digg to meet new friends - I go there for the content.

And as for the amount of recommended stories, digg offers me three to look at. Three stories. Thats it. Out of the thousands and thousands of submissions per day, there are only three that match my digging tastes. Once again, I stress the point that while social networking is nice, social bookmarking is what digg.com is all about.

The difference between the digg recommended story/user algorithm and the RIMA powered algorithm is simple:

Digg - Long Term Relationship: Long term relationships are hit or miss. Sure, you may be happy for the rest of your life, waking up next to the same algorithm everyday, eating the same thing for breakfast, and driving your matching Prius to work. But there is an equal chance that the relationship could end badly. You could catch the algorithm in bed with another byte of code - the entire relationship, built up to one tragic end. Using the social networking road builds a list of friends with (somewhat) similar digging tastes, which in turn gives you a posse of diggers to promote any story your submit. It may work, or it may not. It is hit or miss for every user.

RIMA - One Night Stand: Couples fight, couples bicker, and couples who have been married for a long time never have sex. What is the fun in that? Sometimes I just want to get my digging rocks off to a story that interests me - I do not have the time to meet a bunch of friends and "network" myself around. I go to digg for the stories and thats what I want recommended to me first, users second. In the end I may not have as many friends as I want to, but at least I will be satisfied.

Which method works best for you? Let me know, I am curious.

3:13 AM, Sun Jul 27th 2008
by Peter Oliver

I'm disappointed with Digg's recommendation engine also. I remembered your diggmatch when I saw Digg had released their version. Too bad they didn't branch out to their users like you a bit more to see how people had implemented the concept.

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