Yes. Since each transaction contains several items, you might as well call that a row in the history matrix and go from there to cooccurrence analysis or matrix factorization (cooccurrence is easier and just as accurate if you have enough data).
As Rachel mentions, you also can sometimes string together multiple transactions using additional data such as loyalty programs. That just makes things better. Your assumptions preclude this, but it is often possible, nevertheless. The goal is to make recommendations in real-time at the point of sale just as you suggest. On Fri, Jan 10, 2014 at 10:21 PM, Tim Smith <[email protected]> wrote: > Not sure I follow - are suggesting that the transaction could be thought > of as a proxy for a user? Therefore, each transaction is essentially a > user? And for what end? > > > From: [email protected] > > Date: Fri, 10 Jan 2014 20:06:43 -0800 > > Subject: Re: Item recommendation w/o users or preferences > > To: [email protected] > > > > How is it that you have many transactions and have no user information? > > > > I thought that transactions were user information? > > > > > > On Fri, Jan 10, 2014 at 5:27 PM, Tim Smith <[email protected]> > wrote: > > > > > 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? > > > >
