Recommendation with Mahout

The recommendation engine just gave its first recommendations :)

I converted the taste dump Simon (my mentor) sent to me to a Mahout data source and started a 10-recommendation query for simon@buddycloud.com.

I used an boolean preference user-based recommender, based on a log likelihood user similarity strategy.

The query results follow on: [/channel/beer, /channel/buddycloudscocktailbar, /channel/football, /channel/cebit, /channel/bbc-news, /channel/hangover, /channel/welovemusic, /channel/lyrics, /channel/heavymetal, /user/blugeni@buddycloud.com/channel]

And, according to Simon:

Simon Tennant: hello. Yes, those suggestions seem about right!
Simon Tennant: wow!

For a data source of ~10k users, the query responses are pretty real time.

The next step consists of writing a channel similarity query handler, so new users can get recommendations based on a given channel.