  Nowadays, a hot challenge for supermarket chains is to offer personalized
services for their customers. Next basket prediction, i.e., supplying the
customer a shopping list for the next purchase according to her current needs,
is one of these services. Current approaches are not capable to capture at the
same time the different factors influencing the customer's decision process:
co-occurrency, sequentuality, periodicity and recurrency of the purchased
items. To this aim, we define a pattern Temporal Annotated Recurring Sequence
(TARS) able to capture simultaneously and adaptively all these factors. We
define the method to extract TARS and develop a predictor for next basket named
TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the
level of the customer's stocks and recommend the set of most necessary items.
By adopting the TBP the supermarket chains could crop tailored suggestions for
each individual customer which in turn could effectively speed up their
shopping sessions. A deep experimentation shows that TARS are able to explain
the customer purchase behavior, and that TBP outperforms the state-of-the-art
competitors.
