In recent years, the exponential growth in the number of items and products handled by e-commerce sites has led to the introduction of intelligent systems aimed at supporting users during the decision-making proces. Making the choice of a product among thousands of items becomes complicated for consumers, and in response to this problem, recommender systems (RS) are born. These systems are a set of algorithms based on the concept of information filtering and make it possible to reduce the cognitive effort required of users. In this paper we present a model-based RS, belonging to the collaborative filtering (CF) category, for the e-commerce website of the company Nathan Instruments (NI). Thus, the main objective of this paper is to provide an intelligent approach for recommending configurations of hardware components for Computers. This configurator uses clustering algorithms to address the problems associated with small dataset sizes. Finally, in the experimentation and conclusion sections it is reported how the proposed model simplifies the decision process related to the required computer customization in terms of hardware and software components.

Monticelli, M., Matamoros Aragon, R., Epifania, F., Marconi, L., De Simone, A. (2021). KCRS: KClustering Recommender System for Component Configuration. Intervento presentato a: 1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2021) co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) - 30 November 2021, Virtual, Milan.

KCRS: KClustering Recommender System for Component Configuration

Matamoros Aragon R.;Epifania F.;Marconi L.;
2021

Abstract

In recent years, the exponential growth in the number of items and products handled by e-commerce sites has led to the introduction of intelligent systems aimed at supporting users during the decision-making proces. Making the choice of a product among thousands of items becomes complicated for consumers, and in response to this problem, recommender systems (RS) are born. These systems are a set of algorithms based on the concept of information filtering and make it possible to reduce the cognitive effort required of users. In this paper we present a model-based RS, belonging to the collaborative filtering (CF) category, for the e-commerce website of the company Nathan Instruments (NI). Thus, the main objective of this paper is to provide an intelligent approach for recommending configurations of hardware components for Computers. This configurator uses clustering algorithms to address the problems associated with small dataset sizes. Finally, in the experimentation and conclusion sections it is reported how the proposed model simplifies the decision process related to the required computer customization in terms of hardware and software components.
No
paper
Scientifica
Clustering; E-commerce; Machine Learning; Recommender Systems; Software Components;
English
1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2021) co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) - 30 November 2021
Monticelli, M., Matamoros Aragon, R., Epifania, F., Marconi, L., De Simone, A. (2021). KCRS: KClustering Recommender System for Component Configuration. Intervento presentato a: 1st Italian Workshop on Artificial Intelligence and Applications for Business and Industries (AIABI 2021) co-located with 20th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2021) - 30 November 2021, Virtual, Milan.
Monticelli, M; Matamoros Aragon, R; Epifania, F; Marconi, L; De Simone, A
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/390673
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