当前位置: X-MOL 学术Expert Syst. Appl. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Tourism Recommendation System Based on Semantic Clustering and Sentiment Analysis
Expert Systems with Applications ( IF 8.5 ) Pub Date : 2020-11-21 , DOI: 10.1016/j.eswa.2020.114324
Zahra Abbasi-Moud , Hamed Vahdat-Nejad , Javad Sadri

Numerous number of tourism attractions along with a huge amount of information about them on web and social platforms have made the decision-making process for selecting and visiting them complicated. In this regard, the tourism recommendation systems have become interesting for tourists, but challenging for designers because they should be able to provide personalized services. This paper introduces a tourism recommendation system that extracts users’ preferences in order to provide personalized recommendations. To this end, users reviews on tourism social networks are used as a rich source of information to extract their preferences. Then, the comments are preprocessed, semantically clustered, and sentimentally analyzed to detect a tourist’s preferences. Similarly, all users aggregated reviews about an attraction are utilized to extract the features of these points of interest. Finally, the proposed recommendation system, semantically compares the preferences of a user with the features of attractions to suggest the most matching points of interest to the user. In addition, the system utilizes the vital contextual information of time, location, and weather to filter unsuitable items and increase the quality of suggestions regarding the current situation. The proposed recommendation system is developed by Python and evaluated on a dataset gathered from TripAdvisor platform. The evaluation results show that the proposed system improves the f-measure criterion in comparison with the previous systems.



中文翻译:

基于语义聚类和情感分析的旅游推荐系统

众多的旅游景点,以及在网络和社交平台上有关旅游景点的大量信息,使得选择和访问它们的决策过程变得复杂。在这方面,旅游推荐系统对于游客来说已经变得很有趣,但是对于设计者却具有挑战性,因为他们应该能够提供个性化的服务。本文介绍了一种旅游推荐系统,该系统提取用户的偏好以提供个性化的推荐。为此,用户在旅游社交网络上的评论被用作提取其偏好的丰富信息来源。然后,对注释进行预处理,语义聚类和情感分析,以检测游客的喜好。同样,所有用户对景点的评论汇总都将用于提取这些景点的特征。最后,所提出的推荐系统在语义上将用户的偏好与景点的特征进行比较,以向用户建议最匹配的兴趣点。另外,该系统利用时间,位置和天气的重要上下文信息来筛选不合适的项目,并提高有关当前情况的建议的质量。所提出的推荐系统由Python开发,并在从TripAdvisor平台收集的数据集中进行了评估。评估结果表明,所提出的系统与以前的系统相比,改进了f-measure准则。在语义上将用户的偏好与景点特征进行比较,以向用户建议最匹配的兴趣点。另外,该系统利用时间,位置和天气的重要上下文信息来筛选不合适的项目,并提高有关当前情况的建议的质量。所提出的推荐系统由Python开发,并在从TripAdvisor平台收集的数据集中进行了评估。评估结果表明,所提出的系统与以前的系统相比,改进了f-measure准则。在语义上将用户的偏好与景点特征进行比较,以向用户建议最匹配的兴趣点。另外,该系统利用时间,位置和天气的重要上下文信息来筛选不合适的项目,并提高有关当前情况的建议的质量。所提出的推荐系统由Python开发,并在从TripAdvisor平台收集的数据集中进行了评估。评估结果表明,所提出的系统与以前的系统相比,改进了f-measure准则。和天气来过滤不合适的项目并提高有关当前情况的建议的质量。所提出的推荐系统由Python开发,并在从TripAdvisor平台收集的数据集中进行了评估。评估结果表明,所提出的系统与以前的系统相比,改进了f-measure准则。和天气来过滤不合适的项目并提高有关当前情况的建议的质量。所提出的推荐系统由Python开发,并在从TripAdvisor平台收集的数据集中进行了评估。评估结果表明,所提出的系统与以前的系统相比,改进了f-measure准则。

更新日期:2020-11-22
down
wechat
bug