This talk of support and overlap smacks of very poor coocccurrence analysis.
See http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html for a better option. On Fri, Jan 10, 2014 at 8:05 PM, Tim Smith <[email protected]> wrote: > Very awesome, thank you! I am twisting the support knob right now! > > Sequential analysis in the sense that I A/B test my recommendations and > feed the conversion rates back into my next set of recommendations or > something else? > > > From: [email protected] > > To: [email protected] > > Subject: RE: Item recommendation w/o users or preferences > > Date: Sat, 11 Jan 2014 03:53:41 +0000 > > > > 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 [[email protected]] > > Sent: Friday, January 10, 2014 7:39 PM > > To: [email protected] > > 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: [email protected] > > > To: [email protected] > > > 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.htmlto > get a feel for what Mahout does in the recommendation space. > > > > > > -----Original Message----- > > > From: Tim Smith [mailto:[email protected]] > > > Sent: Friday, January 10, 2014 8:27 PM > > > To: [email protected] > > > 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. > > > > > ============================================================================== > > > >
