When traveling to an unfamiliar city for holidays, tourists often rely on guidebooks, travel websites, or recommendation systems to plan their daily itineraries and explore popular points of interest (POIs). However, these approaches may lack optimization in terms of time feasibility, localities, and user preferences. In this paper, we propose the SBTRec algorithm: a BERT-based Trajectory Recommendation with sentiment analysis, for recommending personalized sequences of POIs as itineraries. The key contributions of this work include analyzing users' check-ins and uploaded photos to understand the relationship between POI visits and distance. We introduce SBTRec, which encompasses sentiment analysis to improve recommendation accuracy by understanding users' preferences and satisfaction levels from reviews and comments about different POIs. Our proposed algorithms are evaluated against other sequence prediction methods using datasets from 8 cities. The results demonstrate that SBTRec achieves an average F1 score of 61.45%, outperforming baseline algorithms. The paper further discusses the flexibility of the SBTRec algorithm, its ability to adapt to different scenarios and cities without modification, and its potential for extension by incorporating additional information for more reliable predictions. Overall, SBTRec provides personalized and relevant POI recommendations, enhancing tourists' overall trip experiences. Future work includes fine-tuning personalized embeddings for users, with evaluation of users' comments on POIs,~to further enhance prediction accuracy.

翻译：在假期前往陌生城市旅行时，游客通常依赖旅游指南、旅行网站或推荐系统来规划每日行程并探索热门景点（POI）。然而，这些方法在时间可行性、地域性和用户偏好方面可能缺乏优化。本文提出SBTRec算法：一种基于BERT并融合情感分析的轨迹推荐方法，用于推荐个性化的POI序列作为旅行路线。本研究的主要贡献包括分析用户的签到数据和上传照片，以理解POI访问与距离之间的关系。我们引入SBTRec，该算法通过情感分析，从用户对不同POI的评论和反馈中理解其偏好与满意度，从而提升推荐准确性。使用来自8个城市的数据集，将所提算法与其他序列预测方法进行对比评估。结果表明，SBTRec的平均F1分数达到61.45%，优于基线算法。本文进一步讨论了SBTRec算法的灵活性——其无需修改即可适应不同场景与城市的能力，以及通过整合额外信息实现更可靠预测的扩展潜力。总体而言，SBTRec提供个性化且相关的POI推荐，提升游客的整体旅行体验。未来工作包括对用户个性化嵌入进行微调，并结合用户对POI的评论评估，以进一步提高预测精度。