Enhancing multilabel classification for food truck recommendation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Adriano Rivolli (Autor) Outra; Carlos Soares (Autor) FEUP; André C. P. L. F. de Carvalho (Autor) Outra

Revista

Título: Expert Systems

Vol. 35 Nº 4

ISSN: 0266-4720

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 4 Citações

ISI Web of Science

Scopus - 7 Citações

INSPEC

Outras Informações

ID Authenticus: P-00P-JQN

DOI: 10.1111/exsy.12304

Abstract (EN): Food trucks are a widely popular fast food restaurant alternative, whose differentiating factor is their proximity to customers. Their popularity has stimulated the expansion of available options, which now includes several different types of cuisines, consequently making the choice by customers a challenging issue. From data obtained via a market research, in which hundreds of participants provided their food truck preferences, this paper focuses on the problem of food truck recommendation using a multilabel approach. In particular, it investigates how to improve the recommendation task regarding a previous work, where some labels have never been predicted. In order to address this problem, different alternatives were investigated. One of these alternatives, the Ensemble of Single Label, proposed in this paper, was able to reduce it. Despite its simplicity, good predictive results were obtained when they were used in the investigated task. Among other benefits, all labels were correctly predicted at least for few instances.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 19