An RFID-Enabled IoT-Based Smart Tourist Route Recommendation Algorithm

Abstract

With the rapid development and broad deployment of Internet of Things (IoT) technologies, the IoT are increasingly shifting away from “interconnection of everything” to “human-computer-thing” sensing integration. Although there are numerous sensing technologies available today, radio frequency identification (RFID) has emerged as useful medium for “passive sensing” due to its lightweight, taggable, and simple deployment properties. With the growth of social networks in recent years, it has become a significant research hotspot for the development of path suggestion systems that are tailored to the demands of individual users’ preferences. This paper considers the relevant features of interest points, integrates the user’s emotion and product similarity into the heuristic function of the ant colony algorithm, adopts the elite management ant strategy, maximizes the management ant strategy, and uses particle swarm algorithm to improve the initial pheromone distribution of the ant colony algorithm. The proposed model combines the ratings of 593 tourists and text comment information into one dataset and proposes a smart tourist route recommendation model. The improved ant colony algorithm is utilized to recommend the most popular tourist routes and recommend the tourist routes of the most popular tourist spots in the scenic area. The suggested method is more efficient in terms of accuracy and recall. The $F$ measure value is derived from real-world dataset testing.