Flavour Enhanced Food Recommendation

We propose a mechanism to use the features of flavour to enhance the quality of food recommendations. An empirical method to determine the flavour of food is incorporated into a recommendation engine based on major gustatory nerves. Such a system has advantages of suggesting food items that the user is more likely to enjoy based upon matching with their flavour profile through use of the taste biological domain knowledge. This preliminary intends to spark more robust mechanisms by which flavour of food is taken into consideration as a major feature set into food recommendation systems. Our long term vision is to integrate this with health factors to recommend healthy and tasty food to users to enhance quality of life.Comment: In Proceedings of 5th International Workshop on Multimedia Assisted Dietary Management, Nice, France, October 21, 2019, MADiMa 2019, 6 page

A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes

Studying food recipes is indispensable to understand the science of cooking. An essential problem in food computing is the adaptation of recipes to user needs and preferences. The main difficulty when adapting recipes is in determining ingredients relations, which are compound and hard to interpret. Word embedding models can catch the semantics of food items in a recipe, helping to understand how ingredients are combined and substituted. In this work, we propose an unsupervised method for adapting ingredient recipes to user preferences. To learn food representations and relations, we create and apply a specific-domain word embedding model. In contrast to previous works, we not only use the list of ingredients to train the model but also the cooking instructions. We enrich the ingredient data by mapping them to a nutrition database to guide the adaptation and find ingredient substitutes. We performed three different kinds of recipe adaptation based on nutrition preferences, adapting to similar ingredients, and vegetarian and vegan diet restrictions. With a 95% of confidence, our method can obtain quality adapted recipes without a previous knowledge extraction on the recipe adaptation domain. Our results confirm the potential of using a specific-domain semantic model to tackle the recipe adaptation task.European Commission 816303University of Granad

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

Exploring the effects of natural language justifications in food recommender systems

Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the user’s characteristics and the recipes’ features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipe’s food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices

Towards persuasive social recommendation: knowledge model

[EN] The exponential growth of social networks makes fingerprint let by users on the Internet a great source of information, with data about their preferences, needs, goals, profile and social environment. These data are distributed across di↵erent sources of information (social networks, blogs, databases, etc.) that may contain inconsistencies and their accuracy is uncertain. Paradoxically, this unprecedented availability of heterogeneous data has meant that users have more information available than they actually are able to process and understand to extract useful knowledge from it. Therefore, new tools that help users in their decision-making processes within the network (e.g. which friends to contact with or which products to consume) are needed. In this paper, we show how we have used a graph-based model to extract and model data and transform it in valuable knowledge to develop a persuasive social recommendation system1.This work was partially supported by the project MINE-CO/FEDER TIN2012-365686-C03-01 of the Spanish government and by the Spanish Ministry of Education, Culture and Sports under the Program for R&D Valorisation and Joint Resources VLC/CAMPUS, as part of the Campus of International Excellence Program (Ref. SP20140788).Palanca Cámara, J.; Heras Barberá, SM.; Jorge Cano, J.; Julian Inglada, VJ. (2015). Towards persuasive social recommendation: knowledge model. ACM SIGAPP Applied Computing Review. 15(2):41-49. https://doi.org/10.1145/2815169.2815173S4149152Desel, J., Pernici, B., Weske, M. 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