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
