面向选择性游览的景区路径推荐算法应用研究

针对目前景区路线推荐基本都从景点的热门程度以及游客可用时间的多少选择旅游路线，很少考虑到游客的个性化需求以及游览效率的现状，提出了一种基于个性化和游览效率的面向选择性游览的景区路径推荐（STRR）算法。首先通过将景区的空间结构离散化，获取游客想要游览的景点；再基于离散粒子群（PSO）算法提出利用优先级规则改进位置更新的方法，快速得到一条满足游客个性化需求的最短路径；最后以北京化工大学东校区为例利用平面仿真进行了实例验证。实验结果表明，STRR算法能够得到一条既满足游客个性化需求且游览效率最高的路径，并在计算效率方面比其他算法具有更好的优越性。

Application of an algorithm for scenic route recommendations during selective sightseeing

Existing popular scenic route recommendations essentially choose a tourist route according to the popularity of the attractions and the available time of the tourists.The recommended routes rarely take either the individual needs of tourists or the efficiency of the tour into account.This paper presents a selective tour for route recommendation (STRR) algorithm for selective tours based on individualization and tour efficiency.This algorithm makes the spatial structure of the scenic area discrete and generates the optimum tourist attractions.Based on the discrete particle swarm optimization (PSO) method,this algorithm proposes a method to improve location update by using the priority rules.It can quickly generate the shortest route which meets the individual needs of tourists.This paper takes the East Campus of Beijing University of Chemical Technology as an example to carry out plane simulations.The experimental results show that the STRR algorithm can generate a route that satisfies the individual needs of tourists whilst also being the most efficient.The new algorithm has been shown to be more efficient in terms of calculation than other algorithms.