Recommendation for e-commerce with a mix of durable and nondurable goods has
characteristics that distinguish it from the well-studied media recommendation
problem. The demand for items is a combined effect of form utility and time
utility, i.e., a product must both be intrinsically appealing to a consumer and
the time must be right for purchase...
In particular for durable goods, time
utility is a function of inter-purchase duration within product category
because consumers are unlikely to purchase two items in the same category in
close temporal succession. Moreover, purchase data, in contrast to ratings
data, is implicit with non-purchases not necessarily indicating dislike. Together, these issues give rise to the positive-unlabeled demand-aware
recommendation problem that we pose via joint low-rank tensor completion and
product category inter-purchase duration vector estimation. We further relax
this problem and propose a highly scalable alternating minimization approach
with which we can solve problems with millions of users and millions of items
in a single thread. We also show superior prediction accuracies on multiple
real-world data sets.