My mail crossed with yours. Try market basket analysis and sequential analysis. With the market basket analysis, there are often a lot of frequent basket combinations that are not that useful. You may want to lower the support to get some more infrequent combinations, but up the confidence level.
Good luck. Rachel ________________________________________ From: Tim Smith [timsmit...@hotmail.com] Sent: Friday, January 10, 2014 7:39 PM To: user@mahout.apache.org Subject: RE: Item recommendation w/o users or preferences 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? > "Email Firewall" made the following annotations. ------------------------------------------------------------------------------ Warning: All e-mail sent to this address will be received by the corporate e-mail system, and is subject to archival and review by someone other than the recipient. This e-mail may contain proprietary information and is intended only for the use of the intended recipient(s). If the reader of this message is not the intended recipient(s), you are notified that you have received this message in error and that any review, dissemination, distribution or copying of this message is strictly prohibited. If you have received this message in error, please notify the sender immediately. ==============================================================================
