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
