Tim, Can you be more specific about which bits are missing?
Is it about the rationale for the log-likelihood ratio test? If so, that rationale is simply that the algorithm is simple and empirically has been shown to produce excellent results across a large array of applications (over a thousand references in the literature now). Is it regarding the specifics of how you do the computation? I can help with that, but would need a pointer to the difficulty. Is it about how to arrange your data to use this computation? The references below might help with that. Is it about the use of indicators for recommendations? If so, here are references that might make that more clear: http://www.linkedin.com/in/sebastianschelter http://ssc.io/wp-content/uploads/2013/02/cf-mahout.pdf http://isabel-drost.de/hadoop/slides/collabMahout.pdf http://www.slideshare.net/tdunning/building-multimodal-recommendation-engines-using-search-engines https://www.youtube.com/watch?v=fWR1T2pY08Y On Sat, Jan 11, 2014 at 7:05 AM, Tim Smith <timsmit...@hotmail.com> wrote: > Ted - I read your blog post and the paper you refer to/wrote and several > other authors that sited your paper, but I am having trouble with the > overall approach for putting all the pieces together. Would you mind > giving me a nudge with the intuition? I realize you have been doing this > for almost 30 years; however, I am just a cave man... Thank you. > > > From: ted.dunn...@gmail.com > > Date: Fri, 10 Jan 2014 20:09:38 -0800 > > Subject: Re: Item recommendation w/o users or preferences > > To: user@mahout.apache.org > > > > This talk of support and overlap smacks of very poor coocccurrence > analysis. > > > > See http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.htmlfor > > a better option. > > > > > > On Fri, Jan 10, 2014 at 8:05 PM, Tim Smith <timsmit...@hotmail.com> > 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: rachel.ows...@safeway.com > > > > To: user@mahout.apache.org > > > > 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 [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.htmltoget > 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. > > > > > > > > > > > > ============================================================================== > > > > > > > > > > > >
