# POIBERT: A Transformer-based Model for the Tour Recommendation Problem

arXiv: https://arxiv.org/abs/2212.13900 · HTML: https://mlsanity.com/paper/2212.13900/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 16 Dec 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019)
- [Learning Points and Routes to Recommend Trajectories](https://mlsanity.com/paper/1608.07051/) (2016)
- [User Preferential Tour Recommendation Based on POI-Embedding Methods](https://mlsanity.com/paper/2103.02464/) (2021)

## Built on by (most cited first, 10 of 10)

- [ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning](https://mlsanity.com/paper/2402.07204/) (2024)
- [A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security](https://mlsanity.com/paper/2410.02191/) (2024)
- [A Survey of Route Recommendations: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2403.00284/) (2024)
- [Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks](https://mlsanity.com/paper/2505.11239/) (2025)
- [BTRec: BERT-Based Trajectory Recommendation for Personalized Tours](https://mlsanity.com/paper/2310.19886/) (2023)
- [SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights](https://mlsanity.com/paper/2302.09938/) (2023)
- [SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis](https://mlsanity.com/paper/2311.11071/) (2023)
- [Utilizing Language Models for Tour Itinerary Recommendation](https://mlsanity.com/paper/2311.12355/) (2023)
- [FewUser: Few-Shot Social User Geolocation via Contrastive Learning](https://mlsanity.com/paper/2404.08662/) (2024)
- [Analyzing and Mitigating Repetitions in Trip Recommendation](https://mlsanity.com/paper/2507.19798/) (2025)

## Abstract (arXiv)

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.