Food tour recommendation using modified ant colony algorithm - Sriphaew, Kritsada; Sombatsricharoen, Kannita (2015)
Food tour is popular and becomes one of the most dynamic and creative segments of tourism.Popular itinerary of food tour can be extracted from the information in the Internet, but preference of the user must also be taken into consideration.This paper proposed a modified Ant Colony algorithm to find best possible itineraries through approximation and heuristic method by taking majority preferences of users into account when computing the recommended itinerary.The experiments were conducted on a food tour of restaurants in Yaowarat, a Bangkok’s China-town of Thailand.The results show that our proposed algorithm can recommend itineraries with rank-accuracy 0.88-0.97, which is better than the original Ant Colony algorithm with rank-accuracy 0.61-0.63.