| dc.description.abstract | With the increase of workloads, the
usage of recommendation platforms for
purchasing meals has increased. The diet
patterns of individuals are influenced by a
multitude of factors including age, health
conditions, pregnancy, culture, religion, and
location. Existing applications recommend
restaurants to the user depending on the user’s
ratings and locations. However, these apps do not
consider personal traits of a user during the
recommendation process, so they cannot provide
effective suggestions that match the user. None of
the existing apps recommend individual food
items that suit the user’s preference. This
research aims to provide a smart solution to this
common issue encountered during online food
purchases. Through the development of a
personalized food recommendation system, the
time spent on selecting food items can be
decreased. This model will be implemented in 2
sections- a mobile application that allows the
users to order food items based on the
recommendations, and a web platform that can
be used by restaurant owners to maintain their
restaurant’s profile. The customized
recommendation process is implemented by
using a hybrid collaborative filtering model, by
addressing the data sparsity and scalability
issues associated with the content-based and
traditional collaborative filtering approaches. | en_US |
