37 research outputs found
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
Multisensory Approaches to Human-Food Interaction
Here, we present the outcome of the 4th workshop on Multisensory Approaches to Human-Food Interaction (MHFI), developed in collaboration with ICMI 2020 in Utrecht, The Netherlands. Capitalizing on the increasing interest on multisensory aspects of human-food interaction and the unique contribution that our community offers, we developed a space to discuss ideas ranging from mechanisms of multisensory food perception, through multisensory technologies, to new applications of systems in the context of MHFI. All in all, the workshop involved 11 contributions, which will hopefully further help shape the basis of a field of inquiry that grows as we see progress in our understanding of the senses and the development of new technologies in the context of food
A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic
This paper addresses the problem of mapping equivalent
items between two databases based on their textual descriptions. Specif-
ically, we will apply this technique to link the elements of two food com-
position databases by calculating the most likely match of each item
in another given database. A number of experiments have been carried
by employing different distance metrics, some of them involving Fuzzy
Logic. The experiments show that the mappings are highly accurate and
Fuzzy Logic improves the precision of the model.European Union under grant
agreement No. 816303 (Stance4Health
Vision-language models boost food composition compilation
Nutrition information plays a pillar role in clinical dietary practice,
precision nutrition, and food industry. Currently, food composition compilation
serves as a standard paradigm to estimate food nutrition information according
to food ingredient information. However, within this paradigm, conventional
approaches are laborious and highly dependent on the experience of data
managers, they cannot keep pace with the dynamic consumer market and resulting
in lagging and missing nutrition data and earlier machine learning methods
unable to fully understand food ingredient statement information or ignored the
characteristic of food image. To this end, we developed a novel vision-language
AI model, UMDFood-VL, using front-of-package labeling and product images to
accurately estimate food composition profiles. In order to drive such large
model training, we established UMDFood-90k, the most comprehensive multimodal
food database to date. The UMDFood-VL model significantly outperformed
convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on a
variety of nutrition value estimations. For instance, we achieved macro-AUCROC
up to 0.921 for fat value estimation, which satisfied the practice requirement
of food composition compilation. This performance shed the light to generalize
to other food and nutrition-related data compilation and catalyzed the
evolution of other food applications.Comment: 31 pages, 5 figure