i am new to mahout. I have already used mahout\'s item based algorithm with a loglikelihood similarity measure. I read in past threads that it is better to use loglikelihood similarity when the reco
I am implementing the Mahout user-based recommendation engine where the recommendations will be served via RecommenderServlet running in Tomcat.
I am using Mahout to build a user-based recommendation system which operates with boolean data. I use GenericBooleanPrefUserBasedRecommender, NearestNUserNeighborhood and now trying to decide about t
I would like to know how stumbleupon recommends articles for its users?. Is it using a neural network or some sort of machine-learning algorithms or is it actually recommending articles based on wha
i looked it up in the internet (ofcourse), and found nothing like it exactly, what I saw was in开发者_Go百科telligent collaborative filtering and simply collaborative filtering. For what it\'s worth,
The problem I\'m trying to solve is finding the right similarity metric, rescorer heuristic and filtration level for my data. (I\'m using \'filtration level\' to mean the amount of ratings that a user
Using collaborative filtering usually applies to giving ratings to an individual user, but how would these algorithms change when nee开发者_运维百科ding to recommend an item(s) to multiple people (for
I\'ve been delving into a lot of recommendation algorithms lately (collaborative filtering mostly) and I\'ve found quite a lot of answers on recommending an item based on either a specific user or ite
I\'m looking for a very simple implementation in Java of a user-based collaborative filtering. I would like to evaluate the precision and recall of this CF with the movielens dataset. I\'ve seen that
I\'m not sure which figures to use below in a problem Im trying to solve that involves using the Pearson Correlation formula.
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