Yes, you do not need to refresh this item-item similarity matrix. The default implementations don't do something like this so I believe you just need to adjust your process to not recompute this on each new user.
It should not be required anyway; a new user entering the system does not need a refresh of item-item similarity. A new *item* might, but, it is no big deal to be missing this item for some short time until the next refresh. If you are reloading a large data set periodically you probably want some system to be constructing the new instance while the old one serves, then switch instances when the new one is ready. Then it doesn't matter much how long it takes to load. Please show a stack trace for NoSuchUserException, at least the portion in the library. On Tue, Jul 13, 2010 at 6:41 AM, samsam <[email protected]> wrote: > Hi again. I'm getting a really hard time to use Taste using the Large 10M > dataset. Using FileDataModel is taking too long to recompute the sim-matrix. > I'm assuming a new user has just arrived (not on the user data or item data) > and starts voting. Trying to refresh the matrix takes over 25 seconds a > large time when considering a website for instance. > > So I found this class. I tried to use it just like shown on javadocs: > > PlusAnonymousUserDataModel plusmodel = new > PlusAnonymousUserDataModel(datamodel); > PearsonCorrelationSimilarity sim = new > PearsonCorrelationSimilarity(datamodel); > Recommender recommender = new GenericItemBasedRecommender(datamodel,sim); > PreferenceArray pref = new GenericUserPreferenceArray(10); > pref.setUserID(0, PlusAnonymousUserDataModel.TEMP_USER_ID); > for(int i=0;i<10;i++){ > pref.setItemID(i, votes[i][0]); > pref.setValue(i,votes[i][1]); > } > synchronized(pref) { > plusmodel.setTempPrefs(pref); > recommender.recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, > 10); > } > > But this is causing an NoSuchUserException at the recommender. > > I tried using the plusmodel on similarity and recommender instead of the > realmodel, but did not work as well. > > > > On Mon, Jul 5, 2010 at 11:52 PM, Sean Owen <[email protected]> wrote: > >> Pre-compute the similarity based on what information? You mention that >> you don't want to use Pearson and mention item attributes. >> >> If you are trying to use domain-specific attributes of items, then >> it's up to you to write that logic. If you want to say books have a >> "0.5" similarity when they are within the same genre, and "0.9" when >> by the same author, you can just write that logic. That's not part of >> the framework. >> >> The hook into the framework comes when you implement ItemSimilarity >> with logic like that. Then just use that ItemSimilarity instead of one >> of the given implementations. That's all. >> >> On Mon, Jul 5, 2010 at 4:32 PM, samsam <[email protected]> wrote: >> > About the second question,I have not the similarity,I want to know is how >> to >> > pre-compute the item similarity. >> > >> > On Mon, Jul 5, 2010 at 11:20 PM, Sean Owen <[email protected]> wrote: >> > >> >> 1) Good question. One answer is to make these "anonymous" users real >> >> users in your data model, at least temporarily. That is, they need not >> >> be anonymous to the recommender, even if they're not yet a registered >> >> user as far as your site is concerned. >> >> >> >> There's a class called PlusAnonymousUserDataModel that helps you do >> >> this. It wraps a DataModel and lets you quickly add a temporary user, >> >> recommend, then un-add that user. It may be the easiest thing to try. >> >> >> >> (BTW the book Mahout in Action covers this in section 5.4, in the >> >> current MEAP draft.) >> >> >> >> 2) Not sure I fully understand. You already have some external, >> >> pre-computed notion of item similarity? then just feed that in to >> >> GenericItemSimilarity and use it from there. >> >> >> >> Sean >> >> >> >> On Mon, Jul 5, 2010 at 1:52 PM, samsam <[email protected]> wrote: >> >> > Hello,all >> >> > I want to build recommendation engine with apache mahout,I have read >> some >> >> > reading material,and I still have some questions. >> >> > >> >> > 1)How to recommend for anonymous users >> >> > I think recommendation engine should return recommendations given a >> item >> >> > id.For example,a anonymous user reviews some items, >> >> > and tell the recommendation what he reviews,and compute with the >> reviews >> >> > histories. >> >> > >> >> > 2)How to compute the items similarity dataset >> >> > Without use items similarity dataset,we can make ItemBasedRecommender >> >> > with PearsonCorrelationSimilarity,but >> >> > we need to make recommendations with extra attributes of items, >> >> > so we should use the items similarity dataset,how to build the dataset >> is >> >> > the key point. >> >> > -- >> >> > I'm samsam. >> >> > >> >> >> > >> > >> > >> > -- >> > I'm samsam. >> > >> > > > > -- > I'm samsam. >
