Continuous collaborative filtering using Mahout?

Any database is too slow to query in real-time, so any approach involves caching the data set in memory, which is what I assume you're already doing with ReloadFromJDBCDataModel Just use refresh() to have it re-load at whatever interval you like. It should do so in the background. The catch is that it will need a lot of memory to load the new model while serving from the old one.

You could roll your own solutions that, say, reload a user at a time.

Any database is too slow to query in real-time, so any approach involves caching the data set in memory, which is what I assume you're already doing with ReloadFromJDBCDataModel. Just use refresh() to have it re-load at whatever interval you like. It should do so in the background.

The catch is that it will need a lot of memory to load the new model while serving from the old one. You could roll your own solutions that, say, reload a user at a time. There's no such thing as real-time updates on Hadoop.

Your best bet there in general is to use Hadoop for full and proper batch computation of results, and then tweak them at run-time (imperfectly) based on new data in the app that is holding and serving recommendations.

Thanks Sean. I'm a little bit puzzled about how Hadoop fits in the overall picture. As far as I understand, it is used to pre-compute the similarities thus leaving the application itself only to do the matching according to the chosen recommender.Is that truly the case?

– Daniel Zohar Nov 21 at 9:34 Hadoop doesn't necessarily have to be a part of this. I would not use Hadoop unless you are forced to by issues of scale. Yes, you could use it for part of the process, computing similarities offline.

– Sean Owen Nov 21 at 11:34.

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