Depending on hardware configuration, exact distribution of ratings over users and items YMMV!
100M preferences are about the data set size where non-distributed recommenders will outgrow a normal-sized machine (32-bit, <= 4GB RAM). Your mileage will vary significantly with the nature of the data.
From the mailing list:
I just finished running a set of recommendations based on the Wikipedia link graph, for book purposes (yeah, it’s unconventional). I ran on my laptop, but it ought to be crudely representative of how it runs in a real cluster.
The input is 1058MB as a text file, and contains, 130M article-article associations, from 5.7M articles to 3.8M distinct articles (“users” and “items”, respectively). I estimate cost based on Amazon’s North American small Linux-based instance pricing of $0.085/hour. I ran on a dual-core laptop with plenty of RAM, allowing 1GB per worker, so this is valid.
In this run, I run recommendations for all 5.7M “users”. You can certainly run for any subset of all users of course.
Phase 1 (Item ID to item index mapping) 29 minutes CPU time $0.05 60MB output
Phase 2 (Create user vectors) 88 minutes CPU time $0.13 Output: 1159MB
Phase 3 (Count co-occurrence) 77 hours CPU time $6.54 Output: 23.6GB
Phase 4 (Partial multiply prep) 10.5 hours CPU time $0.90 Output: 24.6GB
Phase 5 (Aggregate and recommend) about 600 hours about $51.00 about 10GB (I estimated these rather than let it run at home for days!)
Note that phases 1 and 3 may be run less frequently, and need not be run every time. But the cost is dominated by the last step, which is most of the work. I’ve ignored storage costs.
This implies a cost of $0.01 (or about 8 instance-minutes) per 1,000 user recommendations. That’s not bad if, say, you want to update recs for you site’s 100,000 daily active users for a dollar.
There are several levers one could pull internally to sacrifice accuracy for speed, but it’s currently set to pretty normal values. So this is just one possibility.
Now that’s not terrible, but it is about 8x more computing than would be needed by a non-distributed implementation if you could fit the whole data set into a very large instance’s memory, which is still possible at this scale but needs a pretty big instance. That’s a very apples-to-oranges comparison of course; different algorithms, entirely different environments. This is about the amount of overhead I’d expect from distributing – interesting to note how non-trivial it is.
(From the email@example.com mailing list)
I’ve been test-driving a simple application of Mahout recommenders (the non-distributed kind) on Amazon EC2 on the new Yahoo KDD Cup data set (kddcup.yahoo.com).
In the spirit of open-source, like I mentioned, I’m committing the extra code to mahout-examples that can be used to run a Recommender on the input and output the right format. And, I’d like to publish the rough timings too. Find all the source in org.apache.mahout.cf.taste.example.kddcup
(Helpful hint on cost I realized after the fact: you can almost surely get spot instances for cheaper. The maximum price this sort of instance has gone for as a spot instance is about $0.60/hour, vs “retail price” of $1.14/hour.)
Resulted in an RMSE of 29.5618 (the rating scale is 0-100), which is only good enough for 29th place at the moment. Not terrible for “out of the box” performance – it’s just using an item-based recommender with uncentered cosine similarity. But not really good in absolute terms. A winning solution is going to try to factor in time, and apply more sophisticated techniques. The best RMSE so far is about 23.
For this I bothered to write a simplistic item-item similarity metric to take into account the additional info that is available: track, artist, album, genre. The result was comparatively better: 17.92% error rate, good enough for 4th place at the moment.
Of course, the next task is to put this through the actual distributed processing – that’s really the appropriate solution.
This shows you can still tackle fairly impressive scale with a non-distributed solution. These results suggest that the largest instances available from EC2 would accomodate almost 1 billion ratings in memory. However at that scale running a user’s full recommendations would easily be measured in seconds, not milliseconds.