Restaurant recommendation system using particle swarm optimization / Wan Farah Syahirah Wan Ismail

Recommendation system plays an important role in today’s society. As the technology are moving forward people are keener on relying on the automated decision making. Nowadays there are various recommendation systems available all over the world, as the food industry is always expanding the restaurants business are booming as well. It is getting harder to find places to eat as more restaurants are opening making it hard to choose when there are too many options, it can also be time consuming and not all restaurants are properly advertised. This leads to consulting to systems for faster recommendation of places to eat according to user preferences in order to save time and reduce the hassle. Hence, this is why this project is proposed. This project helps people to find places to eat, save time, and at the same time suggesting restaurants according to their preferences. Particle Swarm Optimization is an evolutionary technique that imitates a flock of birds looking for food, it is incorporated in this proposed system as it proven to be good in optimizing. In order to find the most optimal solution, the population of swarm will follow best particle and improve its candidate solution until convergence is reached. From the result conducted from this project, it sure does well in optimizing the best optimal solution even in various situations given. Even so, there are few limitations exist in this project. PSO is very time consuming when executed and the difficulty of accessing it at all times as it is a web-based system.