融合用户特征与群体智慧的多目标旅游线路推荐方法

针对旅游线路推荐过程中的数据稀疏与冷启动问题,本文提出了一种融合用户特征与群体智慧的多目标旅游线路推荐方法。首先,通过携程网、望路行程、百度指数等网站获取景点信息与对应的群体智慧数据,包括景点的位置、票价,用户评论、评分、浏览数据等;其次,结合用户特征与群体智慧数据构建景点对不同特征用户的综合吸引力并计算旅游线路吸引力指数;最后,定义旅游线路推荐多目标优化函数并利用多目标遗传算法NSGA2生成线路推荐列表。相较于传统旅游线路推荐方法,本文所提出的方法充分考虑了用户实际需求（消费侧）与景点吸引力（供给侧）,使得用户能够以较少的时间开销,尽可能多地游览热门景点。同时,推荐过程中根据用户的性别、年龄、出行方式、出行时间对用户群体进行划分,使得推荐准确性更高。实验结果表明,该方法考虑的因子可以有效提高用户在路线规划过程中的满意度,所推荐的旅游线路不仅具有更高的综合吸引力指数,还能够有效减少路程时间。此外,推荐结果也更加具有多样性,有助于推动智能化旅游线路推荐的发展。

Multi-objective Travel-Route Recommendation Method based on Integration of User Features and Group-Intelligence

Improving the accuracy of travel route recommendation is of great significance to the field of tourism recommendation. Considering the problems of data sparsity and cold start involved in the process of travel route recommendation, a multi-objective travel route recommendation method is proposed in this article by integrating user features and group intelligence. First, we obtain scenic spot information and group intelligence data from Ctrip.com, whlyw.net, Baidu Index, and other websites, including the locations of scenic spots, the prices of scenic spots, user comments, user ratings, browsing data, etc. Second, we fuse user features and the group intelligence data to construct the comprehensive attraction index of scenic spots for different kinds of people and calculate the attraction index of travel routes. Finally, a multi-objective optimization function of tourism route recommendation is defined according to the user demand for the most efficient access to scenic spots that are most likely to be of interest to users during travel; thus, the recommendation list is generated by a multi-objective genetic algorithm (non-dominated sorting genetic algorithm-II, NSGA2). Compared to traditional travel route recommendation methods, our proposed method has following advantages: (1) We carefully consider the actual needs of users (consumer side) and the attraction of scenic spots (supplier side), ensuring that users can visit as many popular scenic spots as possible with less travelling time; (2) We consider that scenic spots, especially outdoor scenic spots, show different attraction to tourists in different time periods, and this paper takes into account the travel time of users when calculating the attraction of scenic spots to users; (3) The vehicle selected by a user has a great impact on user travel routes, and our proposed method recommends different travel routes for users according to different vehicles, which better meets user needs; (4) Since different users have different preferences for the same scenic spot, we divide user groups according to user gender, age, trip modes, and trip time, which improves the accuracy of recommendation. The experimental results show that: (1) The factors considered in this paper can effectively improve user satisfaction with the recommendation results; (2) The proposed method considers the factors that can effectively improve user satisfaction during route recommendation and provides diverse recommendation results for users with different features and needs; (3) The proposed method not only gives a higher comprehensive attraction index but also effectively reduces the time spent on the journey. Furthermore, the recommendation results are more diverse, thus contributing to the development of intelligent travel route recommendation.