Examining the Effects of Personalized Explanations in a Multi-list Food Recommender System

In the past decade, food recipe websites have become a popular approach to find a recipe. Due to the vast amount of options, food recommender systems have been devel- oped and used to suggest appetizing recipes. However, recommending appealing meals does not necessarily imply that they are healthy. Recent studies on recommender sys- tems have demonstrated a growing interest in altering the interface, where the usage of multi-list interfaces with explanations has been explored earlier in an unsuccessful at- tempt to encourage healthier food choices. Building upon other research that highlights the ability of personalized explanations to provide a better understanding of presented recommendations, this thesis explores whether a multi-list interface with personalized explanations, which takes into account user preferences, health, and nutritional aspects, can affect users’ evaluation and perception of a food recommender system, as well as steer them towards healthier choices. A food recommender system was develop, with which single- and multi-lists, as well as non-personalized and personalized explana- tions, were compared in an online experiment (N = 163) in which participants were requested to choose recipes they liked and to answer questionnaires. The analysis re- vealed that personalized explanations in a multi-list interface were not able to increase choice satisfaction, choice difficulty, understanding or support healthier choices. Sur- prisingly, users selected healthier recipes if non-personalized rather than personalized explanations were presented alongside them. In addition, users perceived multi-lists to be more diverse and found single-list to be more satisfying.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Consumption of Organic Foods from a Life History Perspective: An Exploratory Study among Danish Consumers

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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

Multi-list Food Recommender Systems for Healthier Choices

Recipe websites are a popular destination for home cooks to discover new recipes and find what to cook. However, the most popular way of recommending recipes to users is trough similarity and popularity-based recommendations, which previous research has shown tend to be unhealthy. Building upon knowledge on how diverse sets of options increases satisfaction, this thesis investigates whether a multi-list recommender interface can support healthier food choices compared to traditional single-list interfaces, as well as increase choice satisfaction. As diverse set of options may introduce choice overload to users, explanations were investigated in terms of how they affect user evaluation with regards to choice difficulty, perceived diversity and understandability. A developed recommender system was used in a online study (N = 366), where users could select recipes from recommendations, as well as answering short questionnaires regarding their choices. The analysis showed that a multi-list recommender system was not able to support healthier food choices. However, users who interacted with the multi-list interface found it more satisfactory compared to single-list users. No significant evidence was found that explanations could mitigate choice difficulty. This thesis provides novel work on the utilization of multi-list recommender systems with explanations in the food recommender domain, which can further be expanded with considering other factors such including personalized recommendations in the multi-list interface.Masteroppgave i informasjonsvitenskapINFO390MASV-INF