mahout-recommender

The latest release mahout 0.9 is only built on hadoop 1.x. (mvn clean install) How can I compile mahout for hadoop 2.0.x? Because When I was running the commands: hadoop jar mahout-examples-0.9-SNAPSHOT-job.jar org.apache.mahout.cf.taste.hadoop.item.RecommenderJob -s SIMILARITY_COOCCURENCE -i test -o result I always got the error message IncompatibleClassChangeError: Found interface org.apache.hadoop.mapreduce.JobContext, but class was expected. Thanks! To compile Mahout to work with 2.x since it isn't released in a package that is compatible with Hadoop 2.x: mvn clean install -Dhadoop2

I want to recommend a user , a list of users which the current user can add as friends. I am using Cassandra and mahout. there is already a implementation of CassandraDataModel in mahout integration package. I want to use this class. So my recommend-er class looks like follows public class UserFriendsRecommender { @Inject private CassandraDataModel dataModel; public List<RecommendedItem> recommend(Long userId, int number) throws TasteException{ UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(dataModel); // Optional: userSimilarity.setPreferenceInferrer(new

For an assignment I'm supposed to test different types of recommenders, which I have to implement first. I've been looking around for a good library to do that (I had thought about Weka at first) and stumbled upon Mahout. I must therefore put forward that: a) I'm completely new to Mahout b) I do not have a strong background in recommenders nor their algorithms (otherwise I wouldn't be doing this class...) and c) sorry but I'm far from being the best developper in the world ==> I'd appreciate if you could use layman terms (as far as possible...) :) I've been following some tutorials (e.g. this

I am puzzled about what the item-based recommendation is, as described in the book " Mahout in Action ". There is the algorithm in the book: for every item i that u has no preference for yet for every item j that u has a preference for compute a similarity s between i and j add u's preference for j, weighted by s, to a running average return the top items, ranked by weighted average How can I calculate the similarity between items? If using the content, isn't it a content-based recommendation? Item-Based Collaborative Filtering The original Item-based recommendation is totally based on user

Does Mahout provide a way to determine similarity between content? I would like to produce content-based recommendations as part of a web application. I know Mahout is good at taking user-ratings matrices and producing recommendations based off of them, but I am not interested in collaborative (ratings-based) recommendations. I want to score how well two pieces of text match and then recommend items that match most closely to text that I store for users in their user profile... I've read Mahout's documentation, and it looks like it facilitates mainly the collaborative (ratings-based)