I doubt Amazon uses a priori for this, but who knows. Usually you want "also bought" functionality, which is a form of similar-item computation. But you don't want to favor items that are simply frequently purchased in general.
You probably want to look at pairs of items that co-occur in purchase histories unusually frequently by looking at (log) likelihood ratios, which is a straightforward item similarity computation. On Fri, Dec 5, 2014 at 11:43 AM, Ashic Mahtab <as...@live.com> wrote: > This can definitely be useful. "Frequently bought together" is something > amazon does, though surprisingly, you don't get a discount. Perhaps it can > lead to offering (or avoiding!) deals on frequent itemsets. > > This is a good resource for frequent itemsets implementations: > http://infolab.stanford.edu/~ullman/mmds/ch6.pdf > > ________________________________ > From: rpuj...@hortonworks.com > Date: Fri, 5 Dec 2014 10:31:17 -0600 > Subject: Re: Market Basket Analysis > To: so...@cloudera.com > CC: t...@preferred.jp; user@spark.apache.org > > > This is a typical use case "people who buy electric razors, also tend to buy > batteries and shaving gel along with it". The goal is to build a model which > will look through POS records and find which product categories have higher > likelihood of appearing together in given a transaction. > > What would you recommend? > > On Fri, Dec 5, 2014 at 7:21 AM, Sean Owen <so...@cloudera.com> wrote: > > Generally I don't think frequent-item-set algorithms are that useful. > They're simple and not probabilistic; they don't tell you what sets > occurred unusually frequently. Usually people ask for frequent item > set algos when they really mean they want to compute item similarity > or make recommendations. What's your use case? > > On Thu, Dec 4, 2014 at 8:23 PM, Rohit Pujari <rpuj...@hortonworks.com> > wrote: >> Sure, I’m looking to perform frequent item set analysis on POS data set. >> Apriori is a classic algorithm used for such tasks. Since Apriori >> implementation is not part of MLLib yet, (see >> https://issues.apache.org/jira/browse/SPARK-4001) What are some other >> options/algorithms I could use to perform a similar task? If there’s no >> spoon to spoon substitute, spoon to fork will suffice too. >> >> Hopefully this provides some clarification. >> >> Thanks, >> Rohit >> >> >> >> From: Tobias Pfeiffer <t...@preferred.jp> >> Date: Thursday, December 4, 2014 at 7:20 PM >> To: Rohit Pujari <rpuj...@hortonworks.com> >> Cc: "user@spark.apache.org" <user@spark.apache.org> >> Subject: Re: Market Basket Analysis >> >> Hi, >> >> On Thu, Dec 4, 2014 at 11:58 PM, Rohit Pujari <rpuj...@hortonworks.com> >> wrote: >>> >>> I'd like to do market basket analysis using spark, what're my options? >> >> >> To do it or not to do it ;-) >> >> Seriously, could you elaborate a bit on what you want to know? >> >> Tobias >> >> >> >> CONFIDENTIALITY NOTICE >> NOTICE: This message is intended for the use of the individual or entity >> to >> which it is addressed and may contain information that is confidential, >> privileged and exempt from disclosure under applicable law. If the reader >> of >> this message is not the intended recipient, you are hereby notified that >> any >> printing, copying, dissemination, distribution, disclosure or forwarding >> of >> this communication is strictly prohibited. If you have received this >> communication in error, please contact the sender immediately and delete >> it >> from your system. Thank You. > > > > > -- > Rohit Pujari > Solutions Engineer, Hortonworks > rpuj...@hortonworks.com > 716-430-6899 > > CONFIDENTIALITY NOTICE > NOTICE: This message is intended for the use of the individual or entity to > which it is addressed and may contain information that is confidential, > privileged and exempt from disclosure under applicable law. If the reader of > this message is not the intended recipient, you are hereby notified that any > printing, copying, dissemination, distribution, disclosure or forwarding of > this communication is strictly prohibited. If you have received this > communication in error, please contact the sender immediately and delete it > from your system. Thank You. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
