Well there are only 7 products in the universe! If you ask for 10 recommendations, you will always get all unrated items back in the recommendations. That's always true unless the algorithm can't actually establish a value for some items.
What result were you expecting, less than 10 recs? less than 7? On Thu, Sep 13, 2012 at 6:55 AM, Gokul Pillai <[email protected]> wrote: > I am trying out Mahout to come up with product recommendations for users > based on data that show what products they use today. > The data is not web-scale, just about 300,000 users and 7 products. Few > comments about the data here: > 1. Since users either have or not have a particular product, the value in > the matrix is either "1" or "0" for all the columns (rows being the userids) > 2. All the users have one basic product, so I discounted this from the > data-model passed to the Mahout recommender since I assume that if everyone > has the same product, its effect on the recommendations are trivial. > 3. The matrix itself is sparse, the total counts of users having each > product is : > A=31847, 54754,1897 | 23154 | 2201 | 2766 | 33585 > > Steps followed: > 1. Created a data-source from the user-product table in the database > File ratingsFile = new > File("datasets/products.csv"); > DataModel model = new FileDataModel(ratingsFile); > 2. Created a recommender on this data > CachingRecommender recommender = new CachingRecommender(new > SlopeOneRecommender(model)); > 3. Loop through all users and get the top ten recommendations: > List<RecommendedItem> recommendations = > recommender.recommend(userId, 10); > > Issue faced: > The problem I am facing is that the recommendations that come out are way > too simple - meaning that all that it seems like what is being recommended > is "if a user does not have product A, then recommend it, if they dont have > product B, then recommend it and so on." Basically a simple inverse of > their ownership status. > > Obviously, I am not doing something right here. How can I do the modeling > better to get the right recommendations. Or is it that my dataset (300000 > users times 7 products) is too small for Mahout to work with? > > Look forward to your comments. Thanks.
