A personalised recommendation of tourism routes based on rule-based reasoning

In order to solve the problems of low accuracy, long time and low satisfaction of tourists existing in traditional methods, this paper proposes a personalised recommendation method of tourism routes based on rule-based reasoning. Firstly, the web crawlers is used to collect travel data and process the collected data dimensionless. Then, the rule reasoning method is used to build the personalised preference model of tourists, and the random gradient rise method is used to optimise the model. Finally, determine the constraints, take the personalised information of tourist routes, scenic spots and tourists as the input vector of the model, build the personalised recommendation model of tourist routes, and get the relevant recommendation results. The experimental results show that the maximum recommendation accuracy of this method is 96%, the recommendation time is always less than 51 ms, and the average tourist satisfaction is 9.70. Copyright © 2024 Inderscience Enterprises Ltd.