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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferencia

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaClustering-based recommender system: Bundle recommendation using matrix factorization to single user and user communities
EditoresTareq Z. Ahram
Páginas330-338
Número de páginas9
ISBN (versión digital)9783319942285
DOI
EstadoPublicada - 1 ene 2019
EventoAdvances in Intelligent Systems and Computing - , Alemania
Duración: 1 ene 2015 → …

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen787
ISSN (versión impresa)2194-5357

Conferencia

ConferenciaAdvances in Intelligent Systems and Computing
País/TerritorioAlemania
Período1/01/15 → …

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