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Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences - 2018

Research Area:  Machine Learning

Abstract:

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 customers decision process: co-occurrence, sequentuality, periodicity, and recurrency of the purchased items. To this aim, we define a pattern TemporalAnnotated 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 customers 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.

Author(s) Name:  Riccardo Guidotti; Giulio Rossetti; Luca Pappalardo; Fosca Giannotti and Dino Pedreschi

Journal name:  IEEE Transactions on Knowledge and Data Engineering

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TKDE.2018.2872587

Volume Information:  Volume: 31, Issue: 11, Nov. 1 2019,Page(s): 2151 - 2163