4 ms·
You've got the basis for a "you-like-this-so-perhaps-you-will-want-to-read-this" recommendation engine. Perform some - mentioned in another reply - n-gram analy
by bulte-rs 14y ago
You've got the basis for a "you-like-this-so-perhaps-you-will-want-to-read-this" recommendation engine. Perform some - mentioned in another reply - n-gram analysis on the corpus; do some basic cosine-similarity analysis with the feeds people subscribe to and see what pops up. Try other techniques (from e.g. ICWSM[1]) (last time I did something like this is April 2007); iterate; analyse results; publish.
At least you'll have fun (YMMV)...
[1] http://www.icwsm.org/[2007-2011] http://www.icwsm.org/[2007-2011]
- robertpohl 14y agoI already use "find similar articles" techniques using Lucene. But I'm thinking it might be more interesting in another more data-centered website?
- dodo53 14y agoDoes your reader record which items in a feed were read? I'm sure lots of feed providers would be interested in metrics on which of their articles were frequently read (and possibly some clustering of interests based on what else readers subscribe to)
- robertpohl 14y agoYes it does! The problem is the low activity on the site. If the traffic grew the meta data would explode ;)