Product Recommendation for Small Medium Enterprises

Alessandro Abluton; Samuele Brondolin; Gianmaria Clerici; Stefano Monti; Luigi PORTINALE; Alessandro Rolando; Andfrea Santomauro; Carlo Zoccola

Product Recommendation for Small Medium Enterprises

Alessandro Abluton
, Samuele Brondolin
, Gianmaria Clerici
, Stefano Monti
, Luigi PORTINALE
, Alessandro Rolando
, Andfrea Santomauro
, Carlo Zoccola

Department of Science and Technological Innovation

Research output: Contribution to conference › Paper › peer-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 for e-shops of SME is to be able to exploit limited amount of data concerning both user interactions and item availability. In this contribution, we describe some approaches based on machine learning techniques that can be exploited even in presence of limited data. They constitute part of a recommendation plugin that is currently developed by INFERENDO srl, an innovative startup which is a spin-off of the University of Piemonte Orientale.

Original language	English
Publication status	Published - 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure

Keywords

Recommender Systems
SME

Access to Document

http://hdl.handle.net/11579/140501

Cite this

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title = "Product Recommendation for Small Medium Enterprises",

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 for e-shops of SME is to be able to exploit limited amount of data concerning both user interactions and item availability. In this contribution, we describe some approaches based on machine learning techniques that can be exploited even in presence of limited data. They constitute part of a recommendation plugin that is currently developed by INFERENDO srl, an innovative startup which is a spin-off of the University of Piemonte Orientale.",

keywords = "Recommender Systems, SME, Recommender Systems, SME",

author = "Alessandro Abluton and Samuele Brondolin and Gianmaria Clerici and Stefano Monti and Luigi PORTINALE and Alessandro Rolando and Andfrea Santomauro and Carlo Zoccola",

year = "2022",

language = "English",

}

TY - CONF

T1 - Product Recommendation for Small Medium Enterprises

AU - Abluton, Alessandro

AU - Brondolin, Samuele

AU - Clerici, Gianmaria

AU - Monti, Stefano

AU - PORTINALE, Luigi

AU - Rolando, Alessandro

AU - Santomauro, Andfrea

AU - Zoccola, Carlo

PY - 2022

Y1 - 2022

N2 - 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 for e-shops of SME is to be able to exploit limited amount of data concerning both user interactions and item availability. In this contribution, we describe some approaches based on machine learning techniques that can be exploited even in presence of limited data. They constitute part of a recommendation plugin that is currently developed by INFERENDO srl, an innovative startup which is a spin-off of the University of Piemonte Orientale.

AB - 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 for e-shops of SME is to be able to exploit limited amount of data concerning both user interactions and item availability. In this contribution, we describe some approaches based on machine learning techniques that can be exploited even in presence of limited data. They constitute part of a recommendation plugin that is currently developed by INFERENDO srl, an innovative startup which is a spin-off of the University of Piemonte Orientale.

KW - Recommender Systems

KW - SME

KW - Recommender Systems

KW - SME

UR - https://iris.uniupo.it/handle/11579/140501

M3 - Paper

ER -

Product Recommendation for Small Medium Enterprises

Abstract

UN SDGs

Keywords

Access to Document

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