Yes, thank you - read through it and several of the item and user recommendation examples. The objective is to recommend based on the current basket - given no users/preferences (but I do have a history of transactions) - I have been able to leverage the item mining algorithm to calculate support and confidence values. When I use a support threshold of 10% and group by product and sort descending on confidence I am left we a ranking of item combos. Basically a top N list by item that I would use to drive the recommendations. In the actual use case, the requirement is not to recommend a product every time, rather the most likely products based on a given basket - with my arbitrary thresholds, I would expect to exclude some baskets.
> From: nimar...@pssd.com > To: user@mahout.apache.org > Subject: RE: Item recommendation w/o users or preferences > Date: Sat, 11 Jan 2014 03:08:30 +0000 > > I think the key question is what is the desired outcome? If you don't have > users (customers) for which you'd like to generate recommendations that > really handcuffs you from a recommendation standpoint. > > I'd recommend starting with a read through this: > http://mahout.apache.org/users/recommender/recommender-first-timer-faq.html > to get a feel for what Mahout does in the recommendation space. > > -----Original Message----- > From: Tim Smith [mailto:timsmit...@hotmail.com] > Sent: Friday, January 10, 2014 8:27 PM > To: user@mahout.apache.org > Subject: Item recommendation w/o users or preferences > > Say I have a retail organization that doesn't sell a diverse set of products, > eg 2000, but has many small transactions. Also say that I don't have any > user or preference information. Is it reasonable to use pattern mining > (market baskets) and recommend items based on a set of thresholds for > support, confidence, and lift? If not, what are my options? >
