Personalized market basket prediction with temporal annotated recurring sequences

Guidotti R., Rossetti G., Pappalardo L., Giannotti F., Pedreschi D.

Adaptation models  Information Systems  Predictive models  Temporal Recurring Sequences  Data Mining  Data mining  History  Interpretable Model  Computational Theory and Mathematics  Analytical models  Next Basket Prediction  Computer Science Applications  User-Centric Model  Market Basket Analysis  Markov processes  Computer Science Applications1707 Computer Vision and Pattern Recognition  Data models 

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. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.

Source: IEEE transactions on knowledge and data engineering (Print) 31 (2019): 2151–2163. doi:10.1109/TKDE.2018.2872587

Publisher: Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America

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