Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

景点推荐在旅游中起着重要作用，例如解决信息超载问题和向用户推荐合适的景点。当前，大多数推荐方法专用于提高推荐的准确性。然而，仅关注准确性的推荐方法倾向于推荐用户经常购买的受欢迎的物品，这导致缺乏多样性并且非受欢迎的物品的可见性低。因此，许多研究表明推荐多样性的重要性并提出了改进的方法，但是仍有改进的空间。首先，对不同项目的多样性的定义需要考虑领域特征。其次，现有的提高多样性的算法牺牲了推荐的准确性。所以，本文利用“吸引力特征”主题来定义推荐多样性的计算方法。我们开发了一个两阶段的优化模型，以增强建议的多样性，同时保持建议的准确性。在第一阶段，提出了一种考虑主题多样性的优化模型，以增加推荐多样性并生成候选吸引力。在第二阶段，我们提出了最小化误分类成本优化模型，以平衡推荐的多样性和准确性。为了评估所提出方法的性能，对真实旅行数据进行了实验。结果表明，所提出的两阶段优化模型可以显着提高建议的多样性和准确性。