  Food recommendation system has proven as an effective technology to provide
guidance on dietary choices, and this is especially important for patients
suffering from chronic diseases. Unlike other multimedia recommendations, such
as books and movies, food recommendation task is highly relied on the context
at the moment, since users' food preference can be highly dynamic over time.
For example, individuals tend to eat more calories earlier in the day and eat a
little less at dinner. However, there are still limited research works trying
to incorporate both current context and nutritional knowledge for food
recommendation. Thus, a novel restaurant food recommendation system is proposed
in this paper to recommend food dishes to users according to their special
nutritional needs. Our proposed system utilises Optical Character Recognition
(OCR) technology and a transformer-based deep learning model, Learning to Rank
(LTR) model, to conduct food recommendation. Given a single RGB image of the
menu, the system is then able to rank the food dishes in terms of the input
search key (e.g., calorie, protein level). Due to the property of the
transformer, our system can also rank unseen food dishes. Comprehensive
experiments are conducted to validate our methods on a self-constructed menu
dataset, known as MenuRank dataset. The promising results, with accuracy
ranging from 77.2% to 99.5%, have demonstrated the great potential of LTR model
in addressing food recommendation problems.
