Enhancing multilabel classification for food truck recommendation

Rívolli, Adriano; Soares, Carlos; Carvalho, André C. P. L. F. de

doi:10.1111/exsy.12304

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…The Missing Label Prediction (MLP) measure (Rivolli et al, 2018) (Equation A17) indicates the proportion of labels that are never predicted by a strategy.…”

Section: Declarationsmentioning

confidence: 99%

Multi-label classification via closed frequent labelsets and label taxonomies

Ferrandin

Cerri

2023

Soft Comput

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Multi-label classification (MLC) is a very explored field in recent years. The most common approaches that deal with MLC problems are classified into two groups: (i) problem transformation which aims to adapt the multi-label data, making the use of traditional binary or multiclass classification algorithms feasible, and (ii) algorithm adaptation which focuses on modifying algorithms used into binary or multiclass classification, enabling them to make multi-label predictions. Several approaches have been proposed aiming to explore the relationships among the labels, with some of them through the transformation of a flat multilabel label space into a hierarchical multi-label label space, creating a tree-structured label taxonomy and inducing a hierarchical multilabel classifier to solve the classification problem. This paper presents a novel method in which a label hierarchy structured as a directed acyclic graph (DAG) is created from the multi-label label space, taking into account the label co-occurrences using the notion of closed frequent labelset. With this, it is possible to solve an MLC task as if it was a hierarchical multi-label classification (HMC) task. Global and local HMC approaches were tested with the obtained label hierarchies and compared with the approaches using tree-structured label hierarchies showing very competitive results. The main advantage of Article Title the proposed approach is better exploration and representation of the relationships between labels through the use of DAG-structured taxonomies, improving the results. Experimental results over 32 multi-label datasets from different domains showed that the proposed approach is better than related approaches in most of the multi-label evaluation measures. Moreover, we found that both tree and in specially DAG-structured label hierarchies combined with a local hierarchical classifier are more suitable to deal with imbalanced multi-label datasets.

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“…The Missing Label Prediction (MLP) measure (Rivolli et al, 2018) (Equation A17) indicates the proportion of labels that are never predicted by a strategy.…”

Section: Declarationsmentioning

confidence: 99%

Multi-label classification via closed frequent labelsets and label taxonomies

Ferrandin

Cerri

2023

Soft Comput

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“…In this paper, we consider the actual usage requirements of users of catering O2O recommendation system [3][4][5][6]. The results of both knowledge filtering and collaborative filtering algorithms are combined and integrated, and a hybrid recommendation method is designed.…”

Section: A Healthy Diet Recommendation Algorithm Incorporating Knowle...mentioning

confidence: 99%

Design of context-based healthy catering O2O recommendation system

Tang

et al. 2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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With the improvement of people's living standards, the public has become more and more concerned about healthy diet. A healthy diet is the first line of defense for human health. The existing recommendation services on catering mostly consider personal preference and usually ignore the people's demand for healthy diet. To solve such problem, we consider the healthy diet recommendation system with balanced healthiness and personalization, where the online and offline interactions of food and beverage are more contextualized and diversified, and the personalized needs of users are context sensitive. In particular, we study the process and system implementation of healthy diet recommendation, and consider the actual user needs of healthy diet recommendation system. By integrating two algorithms of knowledge filtering and collaborative filtering by process, we design a modular system architecture framework and realize the acquisition of contextual information and ontology module with rule-based inference.

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“…Elkobaisi, Maatuk, and Aljawarneh () proposed the extraction of trending topics in search engines to retrieve interesting information that can be used later by recommender systems and other applications. Rivolli, Soares, and de Carvalho () proposed a multilabel method for food truck recommendation. Food trucks are fast food options with diverse services and different locations.…”

Section: Related Workmentioning

confidence: 99%

An efficient hybrid similarity measure based on user interests for recommender systems

et al. 2019

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Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest-user interest similarity measure and the user interest-item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time.

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Enhancing multilabel classification for food truck recommendation

Cited by 9 publications

References 38 publications

Multi-label classification via closed frequent labelsets and label taxonomies

Multi-label classification via closed frequent labelsets and label taxonomies

Design of context-based healthy catering O2O recommendation system

An efficient hybrid similarity measure based on user interests for recommender systems

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