# SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis

arXiv: https://arxiv.org/abs/2311.11071 · HTML: https://mlsanity.com/paper/2311.11071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [An Unsupervised Sentence Embedding Method by Mutual Information Maximization](https://mlsanity.com/paper/2009.12061/) (2020)
- [A Transformer-based Framework for POI-level Social Post Geolocation](https://mlsanity.com/paper/2211.01336/) (2022)
- [POIBERT: A Transformer-based Model for the Tour Recommendation Problem](https://mlsanity.com/paper/2212.13900/) (2022)
- [User Preferential Tour Recommendation Based on POI-Embedding Methods](https://mlsanity.com/paper/2103.02464/) (2021)
- [Sequential Recommendation Model for Next Purchase Prediction](https://mlsanity.com/paper/2207.06225/) (2022)
- [BTRec: BERT-Based Trajectory Recommendation for Personalized Tours](https://mlsanity.com/paper/2310.19886/) (2023)

## Abstract (arXiv)

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.