Tourism route planning recommendation algorithm based on attention mechanism and multi-dimensional portrait scenarios

Abstract

With the continuous improvement of living standards, the tourism industry is also constantly developing. In order to meet the needs of most tourists, relevant management personnel have proposed tourism path planning methods. However, currently, many path planning methods cannot accurately analyze multi-dimensional portraits of tourists, and there are also issues with weak rationality and low tourist satisfaction in tourism path planning. Therefore, this study uses convolutional attention mechanism to optimize the user multi-dimensional portrait scenario recommendation algorithm based on support vector machine and collaborative filtering algorithm. A tourism path planning method is designed for this optimization algorithm. The research first tested the optimization algorithm. The coverage of this optimization algorithm was the widest, with an error rate of only 0.23%, and a recommendation time of only 2.3ms, significantly better than comparison algorithms. The path planning method was analyzed. The path planning accuracy reached 97.1%, the planning time was only 1.2s, and the rationality of the planned tourist route reached 0.95. The satisfaction rate with this route was 97.4%. From the above results, it can be seen that the proposed tourism path planning method has a certain effect on improving tourists’ satisfaction with the tourism route and has a positive impact on the development of the tourism industry.