Have you done any profiling? It would be interesting to know where the bottlenecks are on your dataset.
-Grant On Nov 24, 2009, at 2:37 PM, Otis Gospodnetic wrote: > Correction for the number of user and item data: > Users: 25K > Items: 2K > > I am less worried about increasing the number of potential items to recommend. > I am more interested in getting more users into Taste, so the larger > percentage of my users can get recommendations. > For example, to filter out users I require certain level of activity in terms > of the number of items previously consumed. > With that threshold at 15, I get about 25K users (the above) -- so 25K users > consumed 15 or more items > With 10, I get about 50K users who consumed 10 or more items. > With 5, I get about 200K users who consumed 5 or more items (presumably just > 5 items would produce good-enough recommendations) > > I know I could lower the sampling rate and get more users in, but that feels > like cheating and will lower the quality of recommendations. I have a > feeling even with the sampling rate of 1.0 I should be able to get more users > into Taste and still have Taste give me recommendations in 100-200ms with > only 150-300 reqs/minute. > > > Otis > > > > ----- Original Message ---- >> From: Otis Gospodnetic <[email protected]> >> To: [email protected] >> Sent: Tue, November 24, 2009 2:10:07 PM >> Subject: Taste speed >> >> Hello, >> >> I've been using Taste for a while, but it's not scaling well, and I suspect >> I'm >> doing something wrong. >> When I say "not scaling well", this is what I mean: >> * I have 1 week's worth of data (user,item datapoints) >> * I don't have item preferences, so I'm using the boolean model >> * I have caching in front of Taste, so the rate of requests that Taste needs >> to >> handle is only 150-300 reqs/minute/server >> * The server is an 8-core 2.5GHz 32-bit machine with 32 GB of RAM >> * I use 2GB heap (-server -Xms2000M -Xmx2000M -XX:+AggressiveHeap >> -XX:MaxPermSize=128M -XX:+CMSClassUnloadingEnabled >> -XX:+CMSPermGenSweepingEnabled) and Java 1.5 (upgrade scheduled for Spring) >> >> ** The bottom line is that with all of the above, I have to filter out less >> popular items and less active users in order to be able to return >> recommendations in a reasonable amount of time (e.g. 100-200 ms at the >> 150-300 >> reqs/min rate). In the end, after this filtering, I end up with, say, 30K >> users >> and 50K items, and that's what I use to build the DataModel. If I remove >> filtering and let more data in, the performance goes down the drain. >> >> My feeling is 30K users and 50K items makes for an awfully small data set >> and >> that Taste, esp. at only >> 150-300 reqs/min on an 8-core server should be much faster. I have a >> feeling >> I'm doing something wrong and that Taste is really capable of handling more >> data, faster. Here is the code I use to construct the recommender: >> >> idMigrator = LocalMemoryIDMigrator.getInstance(); >> model = MyDataModel.getInstance("itemType"); >> >> // ItemSimilarity similarity = new LogLikelihoodSimilarity(model); >> similarity = new TanimotoCoefficientSimilarity(model); >> similarity = new CachingUserSimilarity(similarity, model); >> >> // hood size is 50, minSimilarity is 0.1, samplingRate is 1.0 >> hood = new NearestNUserNeighborhood(hoodSize, minSimilarity,similarity, >> model, samplingRate); >> >> recommender = new GenericUserBasedRecommender(model, hood, similarity); >> recommender = new CachingRecommender(recommender); >> >> What do you think of the above numbers? >> >> Thanks, >> Otis > -------------------------- Grant Ingersoll http://www.lucidimagination.com/ Search the Lucene ecosystem (Lucene/Solr/Nutch/Mahout/Tika/Droids) using Solr/Lucene: http://www.lucidimagination.com/search
