Hi, I'm just beginning to play with the Mahout recommendation framework. I'm wondering if I could get some advice for implementing this thing.
My data comes from a web app's, event logs, where the users accounts are only persisted for 30 days -- cookie data. I'm thinking the session ID (in the cookie) would be used as the user ID. The events are tied to product IDs, so these would be used in generating the preferences. I'd like to use the ItemBased recommender, using boolean user preferences. My first question is, how do I generate the similarity data? Do I have to do this using Hadoop? I'm guessing I'll probably want to since, the files are about 1 GB each. I'd like to eventually run this on Hadoop, but it'd also be nice to know if there is a way to do this locally, while developing the app, maybe using a smaller dataset? Thanks in advance for any tips/feedback/help. - Matt
