Kana Maruyama

ACL, 2025 | Kana Maruyama, Angel Hsing-Chi Hwang, Tarek R Besold

Understanding how humans perceive and describe food is essential for NLP applications such as semantic search, recommendation, and structured food communication. However, textual similarity often fails to reflect perceptual similarity, which is shaped by sensory experience, ...

AI4X, 2025 | Tarek R Besold, Uchenna Akujuobi, Samy Badreddine, Jihun Choi, Hatem ElShazly, Frederick Gifford, Kana Maruyama, Kae Nagano, Pablo Sanchez Martin, Thiviyan Thanapalasingam, Alessandra Toniato, Christoph Wehner

Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year [1]. On the one hand, this poses anincreasingly pressing challenge: Effectively navigating this ever-growing body o...

AIR, 2024 | Uchenna Akujuobi, Priyadarshini Kumari, Jihun Choi, Samy Badreddine, Kana Maruyama, Sucheendra K Palaniappan*, Tarek R Besold

Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously imp...

ICCC, 2022 | Kana Maruyama, Michael Spranger

Supporting chefs with ingredient recommender systems to create new recipes is challenging, as good ingredient combinations depend on many factors like taste, smell, cuisine style, texture, chef’s preference and many more. Useful machine learning models do need to be accurate...

IEEE ACCESS, 2021 | Michael Spranger, Kana Maruyama

Countless possibilities of recipe combinations challenge us to determine which additional ingredient goes well with others. In this work, we propose RecipeBowl which is a cooking recommendation system that takes a set of ingredients and cooking tags as input and suggests pos...