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アップデート―Diggのレコメンデーション・エンジン、今週ローンチと確認

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シュワ知事、Teslaとの契約発表ビデオ

Digg は新しいレコメンデーション・エンジン〔ユーザー別にカスタマイズされた記事推薦機能〕に関する情報をいくつかリリースした。 この新機能については、すでにわれわれは昨夜(米国時間6/30)記事を書いて、今週中にローンチされるだろうと予想した。下に概要紹介ビデオ2本をエンベッドしておく。ひとつはDiggの主席コンピュータ科学者、Anton Kastに対するインタビューだ。その後にレコメンデーション・エンジンについてのAnton Kastによる解説(英文)も掲載した。


Digg Recommendation Engine from Kevin Rose on Vimeo.


Anton Talks About The Digg Recommendation Engine from Kevin Rose on Vimeo.



The Digg Recommendation Engine

People love Digg because it’s a place to discover and share great content from around the Web. The Digg homepage always has the most popular stories, but many Digg users find their content in the Upcoming section, which gets over 15,000 new stories a day. To help users filter this enormous amount of content, we have created a new feature: The Digg Recommendation Engine.

When you Digg a story, you tell the Recommendation Engine two things: that you recommend the story to other users and, less obviously, that the users who Dugg the story before you are good at finding content. The Recommendation Engine keeps track of users who Dugg particular stories before you did, and it recommends you the stories they Dugg. The more content you Digg, the smarter the Recommendation Engine becomes.

Finding Diggers Like You The Digg Recommendation Engine uses your Digg history over the last thirty days to make Recommendations. (You can see the number of items you have Dugg over the last month on the right-hand side of the Recommended view.) Every time you Digg a story, the Engine matches you with other Diggers who Dugg the same story, and keeps track of all your Diggs in common with them.

When it’s time to calculate your Recommendations, the Engine draws from this pool of matched Diggers. For each matched Digger, it computes a correlation coefficient between you and them. It then picks a cutoff for this correlation coefficient, and the Diggers who make the cut are called “Diggers Like You.”

It’s easy to understand how the correlations are calculated. For each user with whom you Dugg something in common, the Engine determines how many stories the two of you Dugg in common, and divides that number by the total number of stories you or they Dugg. The ratio is a correlation coefficient, a number between zero and one (zero if you and the other user never agreed; one if you always did). Such a ratio is sometimes called a “Jaccard coefficient.”

This scheme automatically accounts for the overall level of Digging activity. If another user Diggs a lot, they have to agree with you on many stories to become a Digger Like You. If another user Diggs rarely, then a small amount of agreement can suffice. 2 From Diggers Like You to Recommendations Once the Engine has determined your Diggers Like You, your Recommendations consist of stories that your Diggers Like You have already Dugg, minus the stories you already Dugg or Buried. There are some extra steps, like the diversity rules and the promotability constraint described below, but this is the basic idea.

Recommendations are always displayed together with your Diggers Like You and their compatibility percentages. These percentages are just correlation coefficients. You may notice that you are more compatible with a user that has fewer Recommendations than a user with less compatibility but with more Recommendations. This is because although you have Dugg more items in common with the more compatible user, that user has not Dugg as much.

The Recommendations you get from any particular user will come from topics (such as Technology or World News) where you have a shared Digging history. We figure that two users may have similar interests in a subject like ‘playable web games’, but one person might be into politics while the other follows celebrity gossip. So we actually compute correlations, Diggers Like You, and compute Recommendations in several collections of topics independently.

Promotable Stories Since the Recommendation Engine works only with Upcoming stories, all the stories you get from the Recommendation Engine are “promotable”, meaning that they are recent enough to be eligible for the Digg homepage but haven’t appeared there yet. This means that whenever you Digg one of your Recommendations, you are helping select stories for the front page of Digg!

Diversity Just like stories on the homepage, we want your Recommendations to be diverse: a balanced number of stories, not all on the same topic, and not all Dugg by the same people.

To make sure that your Recommendations are diverse, the Engine imposes limits that keep things from getting too focused. It makes sure that no one Digger Like You determines too many of your stories. It attempts to make your Recommendations reflect the spectrum of topics that you’ve Dugg in the past, and it adjusts the compatibility cutoff for Diggers Like You so you don’t get too many or too few stories.

The Engine also limits the influence of any single one of your Diggs. For instance, if you are Digg number 1,000 on a popular story, you will have 999 similar users from that one Digg alone, and those users are not necessarily more compatible with you than the two 3 or three who may have Dugg a less popular story you also liked. The Engine limits the total pool of users you can get from a single Digg to balance things out.

We hope you enjoy using the Recommendation Engine and look forward to helping you uncover even more great stories on Digg!

Digg on!

Anton Kast – Lead Scientist Digg

原文へ

(翻訳: Namekawa, U)