@inproceedings{9b8ff176cfd7422c9ef23ce42ec3b6a6,
title = "Clustering Users by exploiting Activity Tracks in Recommender Systems for SME",
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.",
keywords = "clustering, item recommendation, k-means",
author = "Luigi Portinale and Samuele Brondolin",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2021 ; Conference date: 01-11-2021 Through 03-11-2021",
year = "2021",
doi = "10.1109/ICTAI52525.2021.00214",
language = "English",
series = "Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI",
publisher = "IEEE Computer Society",
pages = "1348--1352",
booktitle = "Proceedings - 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence, ICTAI 2021",
address = "United States",
}