Hi Chantal, I think you're taking the right approach.
I missed the line in your first mail, where you say you have only 10k items, so you definitely won't need hadoop or the RowSimilarityJob. --sebastian Am 19.07.2010 10:27, schrieb Chantal Ackermann: > Hi Sean, > hi Ted, > hi Sebastian, > > thanks a lot for all those detailed answers. I'll need some time to > digest the technical details, I'm afraid. I find Sean's suggestion on > thinking of categories as users and using the recommendation classes for > the task the easiest to understand, right now. > > It's not completely the same situation, though. Or only if thinking of > two user communities, and the recommendations presented to a user of > Community 1 should be from Community 2. > > (@Sebastian) > Each item is categorized in each of the systems but it's allowed that > the item can have zero categories. There are a few hundred categories in > each system. > > The data is in lists of the following structure: > <ITEM (ID)> [List of categories System 1] [List of categories System 2] > > The approach I'll take: > 1. normalize all the cateogory strings and give them unique number > identifiers (unique across both systems, distinct ranges). > 2. walk trough the list and per item: extract one category (= user) and > create a BooleanPreference for that user and item pair. > 3. for each category (System 1) request similar categories (=user > similarity) that are from System 2. I probably have to request a mixed > list (both systems) and filter out the ones from System 1. > > I'll keep you posted. If you have more tipps or things I should take > into account - or if you think that this approach won't return any > decent results I'm glad if you could share. > > Thanks! > Chantal > >
