Food Truck Recommendation Using Multi-label Classification

Abstract

Food trucks are vehicles with which fast food, from various cuisines, is cooked and sold. They have been popular in several countries and usually offer food in different locations of a city. Frequently, several food trucks offer their dishes in music concerts, festivals and other events. When several food trucks are present in a place, the variety of possible cuisines and food dishes makes their choice by the public a challenging task. This paper describes the task of recommending food trucks using a multi-label classification approach, where more than one option can be suggested. The recommendation is made using customers’ personal information and preferences. Six multi-label transformation strategies were used to induce learning models from real data obtained via a market research, where hundreds of participants provided their food preferences. The experimental results show that the strategies overcame the adopted baseline in almost all cases, with RAndom k-labELsets (RAkEL) and Binary Relevance (BR) in specific, were the ones who had the best overall result, respectively. On the other hand, it is required to investigate the matter furthermore to improve the predictive outcome of the task. From a machine learning perspective, a new way to analyze multi-label results, called confusion matrix plot, is discussed and the food truck dataset is released as a new multi-label benchmark.