I have been using mahout-0.1 release version and I am able to get recommendations with datasets roughly 5 million and under but when I attempt 10 million or so no recommendations are given to me. Has anybody had this problem? I'm not sure if I am just using the wrong recommender settings/recommender or if I should just switch to trunk version or something. Ideas? Suggestions?
I have tried item-item recommender, user-item recommenders.... nearest neighborhood... tree clustering.. They all produce numerous recommendations with the smaller data sets. In theory it should only get better with a larger data set. Currently I'm using item-item recommender with caching item similarities and cashing recommender.. ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel); CachingItemSimilarity cis = new CachingItemSimilarity(similarity, dataModel); recommender = new CachingRecommender(new GenericItemBasedRecommender(dataModel, similarity)); ...... I would like to have Mahout to work with 25-50 million rows of data but as of yet 5 million is the best i can do. RAM has also been an issue with larger data sets. -- View this message in context: http://www.nabble.com/Mahout-not-giving-recommendations-with-large-data-sets-tp24956912p24956912.html Sent from the Mahout User List mailing list archive at Nabble.com.
