Clustering Users by exploiting Activity Tracks in Recommender Systems for SME

Luigi Portinale, Samuele Brondolin

Risultato della ricerca: Capitolo in libro/report/atti di convegnoContributo a conferenzapeer review

Abstract

Personalized recommendation of products is an essential feature in any e-commerce service and is becoming more and more important for SME as well. The main problem is to be able to exploit limited amount of data concerning both user interactions and item availability. In the present paper, we introduce and evaluate some k-means based clustering strategies able to recognize different user categories even in presence of limited data. The ability to recognize users on the basis of their activity on the e-commerce site is fundamental to get insights about their preferences, in such a way that suitable products (or categories of products) can be recommended to them. We report on the experiments we have performed in the evaluation of three different clustering strategies, having the goal of grouping users showing similar behavior in interacting with items. We conclude that a standard k-means based on the Frobenius norm of the user matrix can provide good performances in terms of clustering users in the corresponding categories of interest.

Lingua originaleInglese
Titolo della pubblicazione ospiteProceedings - 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence, ICTAI 2021
EditoreIEEE Computer Society
Pagine1348-1352
Numero di pagine5
ISBN (elettronico)9781665408981
DOI
Stato di pubblicazionePubblicato - 2021
Evento33rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2021 - Virtual, Online, United States
Durata: 1 nov 20213 nov 2021

Serie di pubblicazioni

NomeProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2021-November
ISSN (stampa)1082-3409

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???event.eventtypes.event.conference???33rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2021
Paese/TerritorioUnited States
CittàVirtual, Online
Periodo1/11/213/11/21

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