Hi Edith, try the following project: https://github.com/ManuelB/facebook-recommender-demo
On 30.06.2014, at 21:48, Edith Au <[email protected]> wrote: > Hi, > > > I am a newbie and am looking for some guidance to implement my > recommender. Any help would be greatly appreciated. I have a small > data set of location information with the following fields: > neighborhood, amenities, and counts. For example: > > Downtown Gym 15 > Downtown Cafe 50 > … > Midtown Gym 30 > Midtown Cafe 100 > Midtown Bookstore 10 > ... > Financial Dist > … > > > so on and so forth. I want to recommend a neighborhood for a user to > reside base on the amenities (and some other metrics) in his/her > current neighborhood. My understanding is that model-based > recommendation would be a good fit for the job. I would recommend a normal memory based approach for collaborative filtering e.g. based on Cosine or Pearson Correlation. > If I am on the right > track, is there a experimental/beta recommender I can try? Most recommenders in Mahout are stable and have production quality. > > > If there is no such recommender yet, can I still use Mahout for my > project? For example, can I implement my own Similarity which only > computes the similarity between one user's preference to a set of > neighborhood? Yes this is possible you have to implement: https://builds.apache.org/job/mahout-quality/javadoc/org/apache/mahout/cf/taste/similarity/ItemSimilarity.html and/or https://builds.apache.org/job/mahout-quality/javadoc/org/apache/mahout/cf/taste/similarity/UserSimilarity.html > If I understand Mahout correctly, User/Item Similarity > would do N x (N-1) pair of comparisons as oppose to 1 x N comparisons. If you use the Hadoop Job (org.apache.mahout.cf.taste.hadoop.item.RecommenderJob) then Mahout will calculate recommendations for all your users. If you use the normal taste in memory recommenders then only for the current user recommendations are generated. > In my example, User/Item Similarity would compare between Downtown, > Midtown, Fin Dist -- which would be a waste in computation resources > since the comparisons are not needed. You can implement your own CandidateItemsStrategy: http://archive.cloudera.com/cdh4/cdh/4/mahout-0.7-cdh4.3.2/mahout-core/org/apache/mahout/cf/taste/recommender/CandidateItemsStrategy.html to filter the items considered. > > > Thanks in advance for your help. > > Edith Hope that helps Manuel -- Manuel Blechschmidt M.Sc. IT Systems Engineering Schenkendorfstr. 3 56068 Koblenz Mobil: 0173/6322621 Twitter: http://twitter.com/Manuel_B
