We are using Mahout to get UserBased and ItemBased recommendations. We are using a file data model that contains a mapping of userId and itemId (not sorted in any form), Tanimoto Coefficient Similarity and GenericBooleanPrefItemBasedRecommender,
DataModel dataModel = new FileDataModel("/FilePath");
_itemSimilarity = new TanimotoCoefficientSimilarity(dataModel);
_recommender = new CachingRecommender(new GenericBooleanPrefItemBasedRecommender(dataModel,_itemSimilarity));
we also have a rescorer to filter out some of the results, we are calling the inbuilt recommend method of the recommender,
_recommender.recommend(userID, howMany, _rescorer);
We have around 200K users, 55k products and around 4 million entries as user-product preferences. The problem that we are facing is that the first call to recommend method for a user is taking around 300-400ms to return the list of recommended item, which is not a feasible option as per our needs. I am looking for some optimisation techniques that someone has used over mahout, or may be if someone has implemented there own recommend method over the given method, or if we should pass the data after adding some sort to the data files. We are trying to get the recommendation time to be around 100ms. Any suggestions would be really helpful.
