Thanks Owen. Thanks a lot.
You were right, I'm having close to 5000 <User Id>,<Item Id> sets. And my code
is producing recommendations for some User Ids where as for some it does't.
For those User Ids that didn't give any recommendations had only a few
preferences compared to others, but all users do have some preferences.
Guess i need more data to get much accurate recommendations for all users. Is
there any better implementations of this code? any suggestions from your end?
Also, I'd like to get this same code running on my hadoop cluster. Could you
please help me out with some documents or simple code snippets that could lead
my way. I tried web help but most blogs refer me to
org.apache.mahout.cf.taste.hadoop.item.RecommenderJob, the code available with
mahout src. Unfortunately I’m not able to get much from the code as it is a bit
complicated for a starter like me. (I have done a couple of Map reduce project
on text files processing before, that is my hadoop MR experience ).
It would be much helpful if you could share me with some informative tutorials
whichcould make my initial steps in Mahout comfortable before i could start
exploring in depth. I'm from India and I'm eagerly waiting for the Indian
edition of 'Mahout In Action' which is not in stores yet.
Steven,
Thank you for your support.
Thanks and Regards
Bejoy.K.S
> Date: Fri, 12 Nov 2010 12:06:47 +0000
> Subject: Re: Mahout - Help needed - files with no preferences and integarting
> mahout with Hadoop
> From: [email protected]
> To: [email protected]
>
> I think you'd have to debug to get more insight. To me it looks OK. Do you
> have enough data? Maybe your user has few or no prefs, which means nothing
> can be recommended.
>
>
> On Fri, Nov 12, 2010 at 12:02 PM, bejoy ks <[email protected]> wrote:
>
> >
> > Hi Steven
> > I tried my User Similarity recommendation using
> > GenericBooleanPrefUserBasedRecommender as you suggested but unfortunaley it
> > is not resolving my issue. I can see in logs that it is processing the
> > entire file(<UserId, ItemId>) but then in a few seconds the application is
> > terminating without giving me any recommendations. The code i tried is
> >
> > FileDataModel dataModel = new FileDataModel(new File(recsFile));
> > UserSimilarity userSimilarity = new
> > TanimotoCoefficientSimilarity(dataModel);
> > UserNeighborhood neighborhood =new
> > NearestNUserNeighborhood(neighbourhoodSize, userSimilarity, dataModel);
> > Recommender recommender =new
> > GenericBooleanPrefUserBasedRecommender(dataModel, neighborhood,
> > userSimilarity);
> > List<RecommendedItem> recommendations
> > =recommender.recommend(userId, noOfRecommendations);
> > System.out.println(recommendations);
> >
> > I tried the same code with PearsonCorrelationSimilarity as well. Still it
> > not giving me any recommendations.
> > Is there any thing else I should change in my code to get in
> > recommendations?
> >
> > Thanks and Regards
> > Bejoy.K.S
> >
> >
> >
> >
> > > Date: Fri, 12 Nov 2010 10:28:16 +0000
> > > Subject: Re: FW: Mahout - Help needed - files with no preferences and
> > integarting mahout with Hadoop
> > > From: [email protected]
> > > To: [email protected]
> > >
> > > Try using the generic boolean pref user based recommender instead of the
> > > generic user based recommender when using binary data.
> > >
> > >
> > http://mahout.apache.org/javadoc/core/org/apache/mahout/cf/taste/impl/recommender/GenericBooleanPrefUserBasedRecommender.html
> > >
> > >
> > >
> > > On Fri, Nov 12, 2010 at 9:57 AM, bejoy ks <[email protected]> wrote:
> > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > Hi Mahout Experts,
> > > >
> > > >
> > > > I'm totally new into mahout and badly indeed of some expert opinions. I
> > > > wanted
> > > > to implement a recommendation engine using mahout and hadoop, as the
> > > > initial
> > > > stage I just tried a recommender class with mahout alone which worked
> > > > fine . I’m
> > > > using eclipse for my development and to test I just ran the code from
> > my
> > > > eclipse like a simple java class with the required jars already added
> > on
> > > > to project build path. The code I used for testing User Similarity
> > > > recommendation is as follows
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > public static void main(String[] args) throws TasteException
> > > > {
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > String recsFile="D://mahoutFiles//SampleData.txt";
> > > >
> > > >
> > > > int
> > > > neighbourhoodSize=7;
> > > >
> > > >
> > > > long
> > > > ssoId=206008129;
> > > >
> > > >
> > > > int
> > > > noOfRecommendations=5;
> > > >
> > > >
> > > > try {
> > > >
> > > >
> > > > FileDataModel
> > > > dataModel = new FileDataModel(new File(recsFile));
> > > >
> > > >
> > > > UserSimilarity
> > > > userSimilarity = new PearsonCorrelationSimilarity(dataModel);
> > > >
> > > >
> > > > UserNeighborhood
> > > > neighborhood =new NearestNUserNeighborhood(neighbourhoodSize,
> > > > userSimilarity,
> > > > dataModel);
> > > >
> > > >
> > > > Recommender
> > > > recommender =new GenericUserBasedRecommender(dataModel, neighborhood,
> > > > userSimilarity);
> > > >
> > > >
> > > > List<RecommendedItem>
> > > > recommendations =recommender.recommend(ssoId, noOfRecommendations);
> > > >
> > > >
> > > > for
> > > > (RecommendedItem recommendedItem : recommendations) {
> > > >
> > > >
> > > > System.out.println(recommendedItem.getItemID());
> > > >
> > > >
> > > > System.out.print(" Item:
> > > > "+dataModel.getItemIDAsString(recommendedItem.getItemID()));
> > > >
> > > >
> > > > System.out.println(" and
> > > > value: "+recommendedItem.getValue());
> > > >
> > > >
> > > > }
> > > >
> > > >
> > > > } catch (IOException e)
> > > > {
> > > >
> > > >
> > > > e.printStackTrace();
> > > >
> > > >
> > > > } catch (TasteException
> > > > e) {
> > > >
> > > >
> > > > e.printStackTrace();
> > > >
> > > >
> > > > }
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > }
> > > >
> > > >
> > > > The code I used for testing Item Similarity recommendation is as
> > follows
> > > >
> > > > public static void main(String[] args) {
> > > >
> > > > String recsFile="D://mahoutFiles//SampleData.txt";
> > > >
> > > > long ssoId=206008129;
> > > >
> > > > int noOfRecommendations=5;
> > > >
> > > > String itemId="GE-CORPPROG-IMLP-PTOOLS";
> > > >
> > > > try {
> > > >
> > > > //item similarity recommendation based on User
> > > >
> > > > FileDataModel dataModel = new FileDataModel(new
> > File(recsFile));
> > > >
> > > > ItemSimilarity itemSimilarity = new
> > > > LogLikelihoodSimilarity(dataModel);
> > > >
> > > > ItemBasedRecommender recommender =new
> > > > GenericItemBasedRecommender(dataModel, itemSimilarity);
> > > >
> > > > List<RecommendedItem> recommendations
> > > > =recommender.recommend(ssoId, noOfRecommendations);
> > > >
> > > >
> > > >
> > > > for (RecommendedItem recommendedItem : recommendations) {
> > > >
> > > > System.out.println(recommendedItem);
> > > >
> > > > }
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > //item similarity recommendation based on item
> > > >
> > > > recommender =new GenericItemBasedRecommender(dataModel,
> > > > itemSimilarity);
> > > >
> > > > recommendations=
> > > > recommender.mostSimilarItems(dataModel.readItemIDFromString(itemId),
> > > > noOfRecommendations);
> > > >
> > > > System.out.println("Item recommendations based on Item");
> > > >
> > > > for (RecommendedItem recommendedItem : recommendations) {
> > > >
> > > > System.out.println(recommendedItem);
> > > >
> > > > }
> > > >
> > > > } catch (IOException e) {
> > > >
> > > > // TODO Auto-generated catch block
> > > >
> > > > e.printStackTrace();
> > > >
> > > > } catch (TasteException e) {
> > > >
> > > > // TODO Auto-generated catch block
> > > >
> > > > e.printStackTrace();
> > > >
> > > > }
> > > >
> > > >
> > > >
> > > > }
> > > >
> > > >
> > > > The issues or concerns I have are as follows.
> > > >
> > > >
> > > >
> > > > Problem 1:
> > > >
> > > >
> > > > This code produces relevant
> > > > recommendations when I have given my file in the format <User id>,
> > <item
> > > > id>, <preference value>.
> > > > But in my actual sample file I have just the user id and Item id alone,
> > no
> > > > preference value. On my reference since it is kind of a Boolean
> > preference,
> > > > I tried
> > > > using PearsonCorrelationSimilarity
> > > > as well as TanimotoCoefficientSimilarity but it is not giving me any
> > > > recommendations. My actual file format that I'd want to process would
> > be in
> > > > the format <user id>,
> > > > <item id>, . I can see it is processing all users in my file( from my
> > > > eclipse console logs) but no recommendations are being produced as
> > output.
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > Problem 2:
> > > >
> > > >
> > > > I
> > > > want to use my mahout recommender in Hadoop distributed environment. I
> > > > don’t have
> > > > a clue about it. I tried web help but most blogs refer me to
> > > > org.apache.mahout.cf.taste.hadoop.item.RecommenderJob,
> > > > the code
> > > > available with mahout src. Unfortunately I’m not able to get much from
> > > > the code
> > > > as it is a bit complicated for a starter like me. (I have done a couple
> > > > of Map reduce project on text files processing before ). Could someone
> > > > please help me
> > > > out with very basic simple code snippets to run my User Similarity and
> > > > Item Similarity Recommendation code
> > > > on hadoop environment .
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > It would be much
> > > > helpful if you could share me with some informative tutorials which
> > > > could make my initial steps in Mahout comfortable before i could start
> > > > exploring in
> > > > depth.
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > Thank you
> > > >
> > > >
> > > >
> > > >
> > > > Thanks and Regards
> > > > Bejoy.K.S
> > > >
> > > >
> > > >
> >
> >
