SBTRec- A Transformer Framework for Personalized Tour Recommendation
  Problem with Sentiment Analysis
Ngai Lam Ho Roy Ka-Wei Lee Kwan Hui Lim
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.