B = all item-sets gathered from user actions, actions like purchased-together/shopping cart purchases, watchlists etc. i = an item-set vector for a specific user
B: itemSetID, items 1, iPad:iPad-case,stylus 2, iPad:battery-booster:iPad-case [B’B]i = r_i, right? [B’B] would be an item-item cooccurrence similarity matrix taken from item-set actions, calculated using LLR. The items-set IDs are not needed anymore. This would imply that we could create an item-set indicator matrix, then use a user’s item-set as the query to get back an ordered list taken from cooccurrences in other items sets, rather than preference cooccurrences. So instead of summing similar items to each separate item in a shopping cart to get an ordering of items to recommend (the way some people do shopping cart recs) we could use the cooccurrence recommender to get these directly from the items-sets. If the item-set is generated in near realtime we’d need Solr (or some search engine) for the queries. The intuition being that things purchased together at the same time will give you better shopping cart recs than using user preferences generally. The item-sets often have something in common that user history will not lead you to. I suppose you’d have to have a good size chunk of items-sets to make it work. Does this make sense?
