| dc.description.abstract | This study investigated the development of an
event-based food recommendation system tailored to Sri
Lankan culinary traditions, comparing it with existing
systems. The goal of the project was to analyze a way to
fill the demand that present systems mainly fail to meet:
the need for meal suggestions that are both culturally
appropriate and event-specific. In the culinary domain,
food recommendation systems have gained popularity for
their ability to provide personalized dietary advice based
on user behaviour and preferences. Although current
systems, such as those for diabetes self-management,
health-conscious nutrition, and allergen-free infant food,
offer strong personalization, they often lack cultural
sensitivity and the ability to adapt to specific events. This
study examined a variety of approaches, such as contentbased algorithms, collaborative filtering, and machinelearning techniques similar to those employed in
RecipeMate, emphasizing the benefits and drawbacks of
each in terms of contextual and cultural flexibility.
Primary data through surveys and interviews with users
familiar with Sri Lankan cuisine, along with a thorough
literature review, formed the basis of our comparative
analysis. Our findings underscore the need for a
culturally aware recommendation system that caters to
the unique requirements of traditional Sri Lankan events.
The study proposes the development of a novel system
incorporating user feedback, dynamic profiles, and
culturally significant recipes to enhance user satisfaction
and engagement. Future work will focus on testing the system's effectiveness across diverse user groups and integrating it with food delivery and e-commerce
platforms, aiming to set a precedent for similar
applications in other cultural contexts. This approach
seeks to merge modern technological capabilities with
rich cultural traditions, enhancing the culinary
experience for users. | en_US |
