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 named Temporal Annotated Recurring Sequence (TARS). 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. A deep experimentation shows that TARS can explain the customers' purchase behavior, and that TBP outperforms the state-of-the-art competitors.

Market basket prediction using user-centric temporal annotated recurring sequences

Giannotti, Fosca;Pedreschi, Dino
2017

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 customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). 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. A deep experimentation shows that TARS can explain the customers' purchase behavior, and that TBP outperforms the state-of-the-art competitors.
Settore INF/01 - Informatica
2017 IEEE International Conference on Data Mining, ICDM 2017
New Orleans, LA, USA
November 18-21, 2017
2017 IEEE International Conference on Data Mining, ICDM 2017
IEEE Computer Society
978-1-5386-3835-4
978-1-5386-3834-7
consumer behaviour; customer services; data mining; marketing data processing; purchasing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/114514
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