Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities

Remigio Hurtado Ortiz; Rodolfo Bojorque Chasi; César Inga Chalco

doi:10.1007/978-3-319-94229-2_32

Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities

Remigio Hurtado Ortiz, Rodolfo Bojorque Chasi, César Inga Chalco

Artificial Intelligence and Assistive Technologies Research Group (GI-IATa)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

This paper shows the results of a Recommender System (RS) that suggests bundles of items to a user or a community of users. Nowadays, there are several RS that realize suggestions of a unique item considering the preferences of a user. However, these RS are not scalable and sometimes the suggestions that make are far from a user’s preferences. We propose an RS that suggests bundles of items to one user or a community of users with similar affinities. This RS uses an algorithm based on Matrix Factorization (MF). To execute the experiments, we use released databases with high dispersion. The results obtained are evaluated per the metrics Accuracy, Precision, Recall and F-measure. The results demonstrate that the proposed method improves significantly the quality of the suggestions.

Original language	English
Title of host publication	Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities
Editors	Tareq Z. Ahram
Pages	330-338
Number of pages	9
ISBN (Electronic)	9783319942285
DOIs	https://doi.org/10.1007/978-3-319-94229-2_32
State	Published - 1 Jan 2019
Event	Advances in Intelligent Systems and Computing - , Germany Duration: 1 Jan 2015 → …

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	787
ISSN (Print)	2194-5357

Conference

Conference	Advances in Intelligent Systems and Computing
Country/Territory	Germany
Period	1/01/15 → …

Keywords

Bundles of items
Matrix factorization
Recommender system

Access to Document

10.1007/978-3-319-94229-2_32

Cite this

Ortiz, R. H., Chasi, R. B., & Chalco, C. I. (2019). Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. In T. Z. Ahram (Ed.), Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities (pp. 330-338). (Advances in Intelligent Systems and Computing; Vol. 787). https://doi.org/10.1007/978-3-319-94229-2_32

Ortiz, Remigio Hurtado ; Chasi, Rodolfo Bojorque ; Chalco, César Inga. / Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. editor / Tareq Z. Ahram. 2019. pp. 330-338 (Advances in Intelligent Systems and Computing).

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abstract = "This paper shows the results of a Recommender System (RS) that suggests bundles of items to a user or a community of users. Nowadays, there are several RS that realize suggestions of a unique item considering the preferences of a user. However, these RS are not scalable and sometimes the suggestions that make are far from a user{\textquoteright}s preferences. We propose an RS that suggests bundles of items to one user or a community of users with similar affinities. This RS uses an algorithm based on Matrix Factorization (MF). To execute the experiments, we use released databases with high dispersion. The results obtained are evaluated per the metrics Accuracy, Precision, Recall and F-measure. The results demonstrate that the proposed method improves significantly the quality of the suggestions.",

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Ortiz, RH , Chasi, RB & Chalco, CI 2019, Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. in TZ Ahram (ed.), Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. Advances in Intelligent Systems and Computing, vol. 787, pp. 330-338, Advances in Intelligent Systems and Computing, Germany, 1/01/15. https://doi.org/10.1007/978-3-319-94229-2_32

Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. / Ortiz, Remigio Hurtado ; Chasi, Rodolfo Bojorque; Chalco, César Inga.
Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. ed. / Tareq Z. Ahram. 2019. p. 330-338 (Advances in Intelligent Systems and Computing; Vol. 787).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - This paper shows the results of a Recommender System (RS) that suggests bundles of items to a user or a community of users. Nowadays, there are several RS that realize suggestions of a unique item considering the preferences of a user. However, these RS are not scalable and sometimes the suggestions that make are far from a user’s preferences. We propose an RS that suggests bundles of items to one user or a community of users with similar affinities. This RS uses an algorithm based on Matrix Factorization (MF). To execute the experiments, we use released databases with high dispersion. The results obtained are evaluated per the metrics Accuracy, Precision, Recall and F-measure. The results demonstrate that the proposed method improves significantly the quality of the suggestions.

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Ortiz RH , Chasi RB, Chalco CI. Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. In Ahram TZ, editor, Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities. 2019. p. 330-338. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-94229-2_32

Clustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities

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