Intelligent decision support model for recommending restaurant

Abstract

People’s lifestyles began to change, now they tend to be interested in trying various types of culinary practically. The number of restaurants does not mean someone will visit each restaurant, so it is going to depend on various consideration. Here, an intelligent decision support model was developed to help people to get a restaurant suggestion that suitable for them. Seven parameters were adopted scientifically, i.e. customer interest, price/budget, distance between customer and restaurant, taste rating, cleanliness rating, facilities rating, and halal or nonhalal status. Through using the methods fuzzy logic, cosine similarity distance, selection, and optimization (i.e. hybrid Latin hyper-cube-hill-climbing), model is able to provide restaurant recommendation for individual user or group. In this study, the experiment involved 75 restaurants in Jakarta and eight customers.