In some cases users might not get any recommendations. There might be
different reasons of this. In your case there is only item 107 which
can be recommended to user 5 (since user 5 rated all other items).
Item 107 got two ratings which are both 5. In this case pearson
correlation between this item and others are undefined. I think this
is the reason why user 5 is not getting any recommendations.
Tevfik
On Thu, Feb 13, 2014 at 9:08 AM, jobin wilson <jobinwil...@gmail.com> wrote:
> Hi Jiang,
>
> Mahout's userbased recommender make use of similarity of a user with other
> users to arrive at what to recommend to him & in this specific case,uses
> Pearson correlation coefficient calculated from the user ratings as a
> similarity measure to form a neighborhood.It then estimates ratings for
> unpicked items based on user similarity and ratings provided by neighbors.
>
> A short answer is that if a user gets any recommendations totally depend on
> the training data that you provide as input to the model.In this case,if
> you expect 107 as a recommendation for user 5,there arent enough ratings
> available for 107 in the user 5's neighborhood. If you modify your data as
> below,you will get recommendations for user 5. (just add a dummy rating
> 2,107,5)
>
> I have included some code snippet which demonstrate this idea of user
> similarity and neighborhood .Hope this helps.
>
> *Code:*
> public class Test {
>
> public static void main(String args[]) throws Exception {
> String inFile = "F:\\hadoop\\data\\recsysinput.txt";
> DataModel dataModel = new FileDataModel(new File(inFile));
> UserSimilarity userSimilarity = new
> PearsonCorrelationSimilarity(dataModel);
> UserNeighborhood userNeighborhood = new
> NearestNUserNeighborhood(100, userSimilarity, dataModel);
> Recommender recommender = new
> GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);
>
> for (int i = 1; i <= 5; i++) {
> List<RecommendedItem> recommendations =
> recommender.recommend(i, 1);
> for(int j=1;j<=5 ;j++){
> System.out.println("Similarity between user:"+i+" and
> user:"+j+ "= "+userSimilarity.userSimilarity(i, j));
> }
> System.out.println("recommend for user:" + i +" Neighborhood
> Size:" + userNeighborhood.getUserNeighborhood(i).length);
>
> for (RecommendedItem recommendation : recommendations) {
> System.out.println(recommendation);
> }
> }
> }
> }
>
> *Input:*
> 1,101,5.0
> 1,102,3.0
> 1,103,2.5
> 2,101,2
> 2,102,2.5
> 2,103,5
> 2,104,2
> 2,107,5
> 3,101,2.5
> 3,104,4
> 3,105,4.5
> 3,107,5
> 4,101,5
> 4,103,3
> 4,104,4.5
> 4,106,4
> 5,101,4
> 5,102,3
> 5,103,2
> 5,104,4
> 5,105,3.5
> 5,106,4
>
> *Output:*
> SLF4J: Class path contains multiple SLF4J bindings.
> SLF4J: Found binding in
> [jar:file:/D:/from%20D/MSR/Coursework/SEM2/Pattern%20Recognition/project/acadnet/mahout-distribution-0.7/mahout-distribution-0.7/mahout-examples-0.7-job.jar!/org/slf4j/impl/StaticLoggerBinder.class]
> SLF4J: Found binding in
> [jar:file:/D:/from%20D/MSR/Coursework/SEM2/Pattern%20Recognition/project/acadnet/mahout-distribution-0.7/mahout-distribution-0.7/lib/slf4j-jcl-1.6.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
> SLF4J: Found binding in
> [jar:file:/D:/from%20D/MSR/Coursework/SEM2/Pattern%20Recognition/project/acadnet/mahout-distribution-0.7/mahout-distribution-0.7/lib/slf4j-log4j12-1.6.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
> SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an
> explanation.
> log4j:WARN No appenders could be found for logger
> (org.apache.mahout.cf.taste.impl.model.file.FileDataModel).
> log4j:WARN Please initialize the log4j system properly.
> log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for
> more info.
> Similarity between user:1 and user:1= 1.0
> Similarity between user:1 and user:2= -0.7642652566278799
> Similarity between user:1 and user:3= NaN
> Similarity between user:1 and user:4= 0.9999999999999998
> Similarity between user:1 and user:5= 0.944911182523068
> recommend for user:1 Neighborhood Size:3
> RecommendedItem[item:104, value:5.0]
> Similarity between user:2 and user:1= -0.7642652566278799
> Similarity between user:2 and user:2= 0.9999999999999998
> Similarity between user:2 and user:3= 0.8029550685469666
> Similarity between user:2 and user:4= -0.9707253433941515
> Similarity between user:2 and user:5= -0.9393939393939394
> recommend for user:2 Neighborhood Size:4
> RecommendedItem[item:106, value:4.0]
> Similarity between user:3 and user:1= NaN
> Similarity between user:3 and user:2= 0.8029550685469666
> Similarity between user:3 and user:3= 1.0
> Similarity between user:3 and user:4= -1.0
> Similarity between user:3 and user:5= -0.6933752452815484
> recommend for user:3 Neighborhood Size:3
> RecommendedItem[item:106, value:4.0]
> Similarity between user:4 and user:1= 0.9999999999999998
> Similarity between user:4 and user:2= -0.9707253433941515
> Similarity between user:4 and user:3= -1.0
> Similarity between user:4 and user:4= 1.0
> Similarity between user:4 and user:5= 0.8783100656536799
> recommend for user:4 Neighborhood Size:4
> RecommendedItem[item:107, value:5.0]
> Similarity between user:5 and user:1= 0.944911182523068
> Similarity between user:5 and user:2= -0.9393939393939394
> Similarity between user:5 and user:3= -0.6933752452815366
> Similarity between user:5 and user:4= 0.8783100656536799
> Similarity between user:5 and user:5= 1.0
> recommend for user:5 Neighborhood Size:4
> RecommendedItem[item:107, value:5.0]
>
>
>
> On Thu, Feb 13, 2014 at 10:57 AM, Koobas <koo...@gmail.com> wrote:
>
>> 5 should get 107 as a recommendation, whether user-based or item-based.
>> No clue why you're not getting it.
>>
>>
>>
>> On Wed, Feb 12, 2014 at 11:50 PM, jiangwen jiang <jiangwen...@gmail.com
>> >wrote:
>>
>> > Hi, all:
>> >
>> > I try to user mahout api to make recommendations, but I find some userId
>> > has no recommendations, why?
>> >
>> > here is my code
>> > public static void main(String args[]) throws Exception {
>> > String inFile = "F:\\hadoop\\data\\recsysinput.txt";
>> > DataModel dataModel = new FileDataModel(new File(inFile));
>> > UserSimilarity userSimilarity = new
>> > PearsonCorrelationSimilarity(dataModel);
>> > UserNeighborhood userNeighborhood = new
>> > NearestNUserNeighborhood(100, userSimilarity, dataModel);
>> > Recommender recommender = new
>> > GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);
>> >
>> > for (int i = 1; i <= 5; i++) {
>> > List<RecommendedItem> recommendations =
>> > recommender.recommend(i, 1);
>> >
>> > System.out.println("recommend for user:" + i);
>> > for (RecommendedItem recommendation : recommendations) {
>> > System.out.println(recommendation);
>> > }
>> > }
>> > }
>> >
>> >
>> > input data(recsysinput.txt):
>> > 1,101,5.0
>> > 1,102,3.0
>> > 1,103,2.5
>> > 2,101,2
>> > 2,102,2.5
>> > 2,103,5
>> > 2,104,2
>> > 3,101,2.5
>> > 3,104,4
>> > 3,105,4.5
>> > 3,107,5
>> > 4,101,5
>> > 4,103,3
>> > 4,104,4.5
>> > 4,106,4
>> > 5,101,4
>> > 5,102,3
>> > 5,103,2
>> > 5,104,4
>> > 5,105,3.5
>> > 5,106,4
>> >
>> > output:
>> > recommend for user:1
>> > RecommendedItem[item:104, value:5.0]
>> > recommend for user:2
>> > RecommendedItem[item:106, value:4.0]
>> > recommend for user:3
>> > RecommendedItem[item:106, value:4.0]
>> > recommend for user:4
>> > RecommendedItem[item:105, value:5.0]
>> > recommend for user:5
>> >
>> > UserId 5 has no recommendations, is it right?
>> > Can I get some recommendations for userId 5, even if the recommendation
>> > results are not good enough?
>> >
>> > thanks
>> > Regards!
>> >
>>
