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. Considering the locations, sightseeing, and travel time between consecutive Pots, our approach incorporates individual user preferences through the utilization of historical data. The key contributions of this work include analyzing users’ check-ins and uploaded photos to understand the relationship between Pot visits and distance. In addition, SBTREC also encompasses sentiment analysis to improve recommendation accuracy by understanding users’ preferences and satisfaction levels from reviews and comments about different Pots. Our proposed algorithms are evaluated against other sequence prediction methods using datasets from 8 cities. The results demonstrate that SBTREC achieves an average $\mathcal{F}_{1}$ 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 Pot recommendations, enhancing tourists’ overall trip experiences. Future work includes fine-tuning personalized embeddings for users, with evaluation of users’ comments on Pots, to further enhance prediction accuracy.

Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Interest (POIs) but their solutions may not align well with the user's own preferences and other location constraints. Additionally, these solutions do not take into consideration of the users' preference based on their past POIs selection. In this paper, we propose POIBERT, an algorithm for recommending personalized itineraries using the BERT language model on POIs. POIBERT builds upon the highly successful BERT language model with the novel adaptation of a language model to our itinerary recommendation task, alongside an iterative approach to generate consecutive POIs.Our recommendation algorithm is able to generate a sequence of POIs that optimizes time and users’ preference in POI categories based on past trajectories from similar tourists. Our tour recommendation algorithm is modeled by adapting the itinerary recommendation problem to the sentence completion problem in natural language processing (NLP). We also innovate an iterative algorithm to generate travel itineraries that satisfies the time constraints which is most likely from past trajectories. Using a Flickr dataset of seven cities, experimental results show that our algorithm out-performs many sequence prediction algorithms based on measures in recall, precision and F1-scores.