问题
I have a database full of two different types of users (Mentors and Mentees), whereby I want the second group (Mentees) to be able to "search" for people in the first group (Mentors) who match their profile. Mentors and Mentees can both go in and change items in their profile at any point in time.
Currently, I am using Apache Mahout for the user matching (recommender.mostSimilarIDs()). The problem I'm running into is that I have to reload the user data every single time anyone searches. By itself, this doesn't take that long, but when Mahout processes the data it seems to take a very long time (14 minutes for 3000 Mentors and 3000 Mentees). After processing, matching takes mere seconds. I also get the same INFO message over and over again while it's processing ("Processed 2248 users"), while looking at the code shows that the message should only be outputted every 10000 users.
I'm using the GenericUserBasedRecommender and the GenericDataModel, along with the NearestNUserNeighborhood, AveragingPreferenceInferrer and PearsonCorrelationSimilarity. I load mentors from the database, add the mentee to the list of POJOs and convert them to a FastByIDMap to give to the DataModel.
Is there a better way to be doing this? The product owner needs the data to be current for every search.
回答1:
(I'm the author.)
You shouldn't need to ask it to reload the data every time, why's that?
14 minutes sounds way, way too long to load such a small amount of data too, something's wrong. You might follow up with more info at user@mahout.apache.org.
You are seeing log messages from a DataModel, which you can disable in your logging system of choice. It prints one final count. This is nothing to worry about.
I would advise you against using a PreferenceInferrer unless you absolutely know you want it. Do you actually have ratings here? I might suggest LogLikelihoodSimilarity if not.
来源：https://stackoverflow.com/questions/3574929/user-matching-with-current-data