Articolo in rivista, 2019, ENG, 10.1109/TKDE.2018.2872587
Guidotti R.; Rossetti G.; Pappalardo L.; Giannotti F.; Pedreschi D.
ISTI-CNR, Pisa, Italy; Università di Pisa, Pisa, Italy; ISTI-CNR, Pisa, Italy; ISTI-CNR, Pisa, Italy; ISTI-CNR, Pisa, Italy; ISTI-CNR, Pisa, Italy; Università di Pisa, Pisa, Italy
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.
IEEE transactions on knowledge and data engineering (Print) 31 (11), pp. 2151–2163
Next Basket Prediction, Temporal Recurring Sequences, User-Centric Model, Market Basket Analysis, Interpretable Model
Pedreschi Dino, Giannotti Fosca, Rossetti Giulio, Pappalardo Luca, Guidotti Riccardo
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 397162
Year: 2019
Type: Articolo in rivista
Creation: 2019-01-02 10:27:32.000
Last update: 2021-04-06 14:04:17.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1109/TKDE.2018.2872587
URL: https://ieeexplore.ieee.org/abstract/document/8477157/keywords#keywords
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:397162
DOI: 10.1109/TKDE.2018.2872587
Scopus: 2-s2.0-85054379547
ISI Web of Science (WOS): 000500304600009