Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market 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 of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, 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 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.
R. Guidotti, G. Rossetti, L. Pappalardo, F. Giannotti, D. Pedreschi "Market Basket Prediction using User-Centric Temporal Annotated Recurring Sequences", IEEE International Conference on Data Mining, 2017. In 2017 IEEE International Conference on Data Mining (ICDM) (pp. 895-900). IEEE.
R. Guidotti, G. Rossetti, L. Pappalardo, F. Giannotti, D. Pedreschi "Next Basket Prediction using Recurring Sequential Patterns", arXiv:1702.07158 [cs.DB], 2017