The framework is pretty general, so yeah you can get it to do most anything, though some things might need more custom code than others.
Viewed generally, a recommender takes as input associations from As to Bs, and then given an A, predicts new associations to Bs. Usually we think of As as users and Bs and items. But you could let As be browsed items, and Bs be items that were ultimately purchased by users who browsed A. Then this is a recommender problem, not merely a simpler most-similar-items problem. Given an item being browsed, you can recommend items that are most likely to be purchased. The work you'd have to do is simply assembling these associations in the first place. You'd dig through your purchase and browsing data, and output all item-item pairs where item 1 is a browsed item and item 2 is an item that was ultimately purchased by one or more users who browsed the first item. The value might be the number of users who fit this description. Once you have that input you can throw any of the recommenders at it to produce the output. You'd have more choice, including distributed recommenders, and have access to evaluators as well. No custom code ought to be needed unless you want to. On Thu, Apr 15, 2010 at 1:10 PM, Sebastian Feher <sfe...@crossview.com> wrote: > There are a few questions that I'm not able to answer: > - do you support cross-type frequent item sets? for example - people who > Browsed this item - ended up purchasing these items. In this case the item > pairs are generated by taking one item from the Browse space and the other > from Purchase space. Is this something that can be achieved with the current > algorithms(GenericItemBasedRecommender.mostSimilarItems(), FP-Growth) in > there existing form and if not there an extension mechanism that allows me to > do that in a clean fashion or do I have to modify the algorithm code?
