I have two questions concerning User Based Recommenders and Item Based Recommenders:
USER RECOMMENDER QUESTION: In a User Based Recommender (in production after the model is computed), I will receive a query for a User Based Recommendation that is based on newly generated User Preference Data. >From my study so far it seems like I should try TreeClusteringRecommender to start with, but how do I use the users most recent preference data to generate a result in real time (within a single web transaction)? I need to update the model in each query right? E.g. myTreeClusteringRecommender.refresh()? ITEM RECOMMENDER QUESTION: In an Item Based Recommender I can call recommender.mostSimilar(itemIDs) with a set of items that the user has expresses preference for (most recent preference data). Is there a way I can weight these preferences? For example a user might have already clicked on 2 items, and just looked at 3 others. If this is my itemIDs set, the first two should affect the recommendation more than the other 3.
