随着互联网的快速发展, 推荐系统的研究和应用朝着多方向、多领域 发展, 传统的推荐算法已经不再满足某些特定领域准确率的要求.考虑到用户在线评论信息可以获得用户对产品偏好信息及偏好程度, 文章提出一种基于用户在线评论的旅游景点推 荐算法.首先, 该算法用爬虫软件和Jieba分词对用户旅游景点的在线评论信息进行获取和预处理.其次, 利用情感强度分析法确定每条评论相对景点各属性的评价标度.再次, 依据处理后的在线评论信息计算用户对景点各属性的权重.最后, 利用TOPSIS排序方法实现对用户旅 游景点的推荐.实验表明, 该算法可以有效的确定用户对景点的偏好程度, 有效提升旅游景点推荐的准确性.

With the rapid development of the Internet, the research and application of recommendation systems are developing in multiple directions and fields. Traditional recommendation algorithms no longer meet the requirements for accuracy in some specific fields. Considering that the user's online review information can obtain the user's preference information and preference for the product, this article proposes a tourist attraction recommendation algorithm based on the user's online review. First, crawler software and Jieba word segmentation are used to obtain and preprocess the online review information of users visiting tourist attraction. Second, the sentiment intensity analysis method is used to determine the evaluation scale of each comment's relative attractions and attributes. Third, the user's weight on the attributes of the tourist attraction is calculated based on the processed online review information. Finally, the TOPSIS ranking method is used to recommend users' tourist attractions. Experiments show that the algorithm can effectively determine the user's preference for attractions and effectively improve the accuracy of tourist attraction recommendations.