Vermont Agriculture and Food System Plan 2020 -- A Review of Recommendations (Part One)

Key Findings in reviewing the Vermont Agriculture and Food System Plan: 1. All recommendations in this review have been coded into eight thematic categories to be used more effectively by stakeholders. 2. We identify four clusters of recommendations to assist stakeholders in understanding the relationships between categories and enabling understanding of the various stakeholders and resources necessary to implement recommendations from different briefs 3. 87% of recommendations either request direct funding for an initiative or recommend a capital expenditure. With financial challenges amidst COVID-19, we highlight eight recommendations for a Vermont Food System that could move forward without financial resources. 4. In the future, giving authors a guide for writing recommendations would make them easier to categorize and implement

Context-aware food recommendation system

Recommendation systems are commonly used in websites with large datasets, frequently used in e-commerce or multimedia streaming services. These systems effectively help users in the task of finding items of their interest, while also being helpful from the perspective of the service or product provider. However, successful applications to other domains are less common, and the number of personalized food recommendation systems is surprisingly small although this particular domain could benefit significantly from recommendation knowledge. This work proposes a contextaware food recommendation system for well-being care applications, using mobile devices, beacons, medical records and a recommender engine. Users passing near a food place receives food recommendation based on available offers order by appropriate foods for everyone’s health at the table in real time. We also use a new robust recipe recommendation method based on matrix factorization and feature engineering, both supported by contextual information and statistical aggregation of information from users and items. The results got from the application of this method to three heterogeneous datasets of recipe’s user ratings, showed that gains are achieved regarding recommendation performance independently of the dataset size, the items textual properties or even the rating values distribution.info:eu-repo/semantics/publishedVersio

Engaging end-user driven recommender systems: personalization through web augmentation

In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.Fil: Wischenbart, Martin. Johannes Kepler University Linz; AustriaFil: Firmenich, Sergio Damian. Universidad Nacional de La Plata. Facultad de Informática. Laboratorio de Investigación y Formación en Informática Avanzada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Rossi, Gustavo Héctor. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Bosetti, Gabriela Alejandra. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Kapsammer, Elisabeth. Johannes Kepler University Linz; Austri

Enhancing Efficiency of the Nutrition Education for Utah Refugees

Refugees face many challenges related to obtaining and preparing adequate and culturally acceptable and desirable foods in their resettlement communities. These challenges often lead to risk of food insecurity and chronic diseases. A review of literature identified the existing delivery strategies and highlighted the need for nutrition education tailored to the refugees’ conditions. This study identified some of the barriers that refugees face, including the English language, transportation, finding items in grocery stores, availability of ingredients and equipment needed for cooking, affordability of food and budgeting. It also highlighted participants’ preference for having nutrition education. To overcome these challenges, the Create Better Health (CBH) curriculum was adapted to be culturally relevant for Somali refugees in Utah. The adapted curriculum was used to teach Somali refugees in Utah about nutrition. This nutrition education was delivered online during a 2-hour session once per week for 12 weeks by a Somali professional. Pre- and post-intervention survey data demonstrated that several nutrition-related behaviors improved among the participants after receiving the intervention. Although many nutrition-related behaviors improved, most of the participants still were not meeting recommended levels of fruits and vegetables and most of the other indicators of the five domains

Computational intelligent methods for trusting in social networks

104 p.This Thesis covers three research lines of Social Networks. The first proposed reseach line is related with Trust. Different ways of feature extraction are proposed for Trust Prediction comparing results with classic methods. The problem of bad balanced datasets is covered in this work. The second proposed reseach line is related with Recommendation Systems. Two experiments are proposed in this work. The first experiment is about recipe generation with a bread machine. The second experiment is about product generation based on rating given by users. The third research line is related with Influence Maximization. In this work a new heuristic method is proposed to give the minimal set of nodes that maximizes the influence of the network