I'd like to experiment with using several types of implicit preference values with recommenders. I have purchases as an implicit pref of high strength. I'd like to see if add-to-cart, view-product-details, impressions-seen, etc. can increase offline precision in holdout tests. These less than obvious implicit prefs will get a much lower value than purchase and i'll experiment with different mixes. The problem is that some of these prefs will indicate that the user, for whom I'm getting recs, has expressed a preference.
Using these implicit prefs seems reasonable in finding similarity of taste between users but presents several problems. 1) how to encode the prefs, each impression-seen will increase the strength of preference of a user for an item but the recommender framework replaces the preference value for items preferred more than once, doesn't it? 2) AFAIK the current recommender framework will return recs only for items that the user in question has expressed no preference for. If I use something like view-product-details or impressions-seen, I will be removing anything the user has seen from the recs, which is not what I want in this experiment. Has anyone tried something like this? I'm not convinced that these other implicit preferences will add anything to the recommender, just trying to find out.
