A Food Recommender System in Academic Environments Based on Machine Learning Models

Background: People's health depends on the use of proper diet as an important factor. Today, with the increasing mechanization of people's lives, proper eating habits and behaviors are neglected. On the other hand, food recommendations in the field of health have also tried to deal with this issue. But with the introduction of the Western nutrition style and the advancement of Western chemical medicine, many issues have emerged in the field of disease treatment and nutrition. Recent advances in technology and the use of artificial intelligence methods in information systems have led to the creation of recommender systems in order to improve people's health. Methods: A hybrid recommender system including, collaborative filtering, content-based, and knowledge-based models was used. Machine learning models such as Decision Tree, k-Nearest Neighbors (kNN), AdaBoost, and Bagging were investigated in the field of food recommender systems on 2519 students in the nutrition management system of a university. Student information including profile information for basal metabolic rate, student reservation records, and selected diet type is received online. Among the 15 features collected and after consulting nutrition experts, the most effective features are selected through feature engineering. Using machine learning models based on energy indicators and food selection history by students, food from the university menu is recommended to students. Results: The AdaBoost model has the highest performance in terms of accuracy with a rate of 73.70 percent. Conclusion: Considering the importance of diet in people's health, recommender systems are effective in obtaining useful information from a huge amount of data. Keywords: Recommender system, Food behavior and habits, Machine learning, Classificatio

Application of choice models in tourism recommender systems

Choice models (CM) are proposed in the field of tourism recommender systems (TRS)with the aim of providing algorithms with both a theoretical understanding of tour-ist's motivations and a certain degree of transparency. The goal of this work is toovercome some of the limitations of current state-of-art algorithms used in TRSs byproviding: (1) accurate preferences, which are learnt from user choices rather thanfrom ratings, and (2) interpretable coefficients, which are achieved by means of theset of estimated parameters of CM. The study was carried out with a gastronomicdata set generated in an ecological experiment in the tourism domain. The perfor-mance of CM has been compared with a set of baseline algorithms (rating-based andensembles) by using two evaluation metrics: precision and DCG. The CM out-performed the baseline algorithms when the size of the choice set was limited. Thefindings suggest that CM may provide an optimal trade-off between theoreticalsoundness, interpretability and performance in the field of TRSThis research was sponsored by EMALCSA/Coruña Smart City under grant CSC-14-13, the Ministry of Science and Innovation of Spain under grant TIN2014-56633-C3-1-R, the Ministry of Economy and Competitiveness of Spain under grant MTM2013-41383P, the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08), and the European Regional Development Fund (ERDF)S

GreedyBoost: An Accurate, Efficient and Flexible Ensemble Method for B2B Recommendations

Recommender systems have achieved great success in finding relevant products and services for individual customers, e.g. in B2C markets, during recent years. \ However, due to the diversity of enterprise clients\u27 requirements it is still an open question on how to successfully apply existing recommendation techniques in the B2B domain. \ \ This paper presents GreedyBoost --- an accurate, efficient and flexible ensemble method for product and service recommendations in the B2B domain. Given a set of base models, GreedyBoost can sequentially add base models to the ensemble by a linear approach to minimize training error, so that the ensemble process is efficient. Meanwhile, GreedyBoost does not have any special requirement on base models and evaluation metrics, so that any kind of client requirements and sale \\& distribution purposes can be adapted. Experimental results on real-world B2B data demonstrate that GreedyBoost can achieve higher recommendation accuracy compared with two popular ensemble methods

A data mining approach to guide students through the enrollment process based on academic performance

Student academic performance at universities is crucial for education management systems. Many actions and decisions are made based on it, specifically the enrollment process. During enrollment, students have to decide which courses to sign up for. This research presents the rationale behind the design of a recommender system to support the enrollment process using the students’ academic performance record. To build this system, the CRISP-DM methodology was applied to data from students of the Computer Science Department at University of Lima, Perú. One of the main contributions of this work is the use of two synthetic attributes to improve the relevance of the recommendations made. The first attribute estimates the inherent difficulty of a given course. The second attribute, named potential, is a measure of the competence of a student for a given course based on the grades obtained in relatedcourses. Data was mined using C4.5, KNN (K-nearest neighbor), Naïve Bayes, Bagging and Boosting, and a set of experiments was developed in order to determine the best algorithm for this application domain. Results indicate that Bagging is the best method regarding predictive accuracy. Based on these results, the “Student Performance Recommender System” (SPRS) was developed, including a learning engine. SPRS was tested with a sample group of 39 students during the enrollment process. Results showed that the system had a very good performance under real-life conditions