# Out-of-Town Recommendation with Travel Intention Modeling

arXiv: https://arxiv.org/abs/2101.12555 · HTML: https://mlsanity.com/paper/2101.12555/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Neural Variational Inference for Text Processing](https://mlsanity.com/paper/1511.06038/) (2015)
- [Discovering Discrete Latent Topics with Neural Variational Inference](https://mlsanity.com/paper/1706.00359/) (2017)
- [Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction](https://mlsanity.com/paper/1911.10516/) (2019)
- [Topic Memory Networks for Short Text Classification](https://mlsanity.com/paper/1809.03664/) (2018)
- [Spatial Object Recommendation with Hints: When Spatial Granularity Matters](https://mlsanity.com/paper/2101.02969/) (2021)
- [Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation](https://mlsanity.com/paper/1905.13127/) (2019)
- [A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users](https://mlsanity.com/paper/1706.06239/) (2017)
- [Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach](https://mlsanity.com/paper/1712.03087/) (2017)

## Built on by (most cited first, 6 of 6)

- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.07359/) (2021)
- [MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal](https://mlsanity.com/paper/2107.05180/) (2021)
- [UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction](https://mlsanity.com/paper/2306.11443/) (2023)
- [Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation](https://mlsanity.com/paper/2310.18606/) (2023)
- [An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark](https://mlsanity.com/paper/2608.27840/) (2026)

## Abstract (arXiv)

Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town check-in behavior is determined by not only the user's home-town preference but also the user's travel intention. Besides, the user's travel intentions are complex and dynamic, which leads to big difficulties in understanding such intentions precisely. In this paper, we propose a TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The proposed TRAINOR framework distinguishes itself from existing out-of-town recommenders in three aspects. First, graph neural networks are explored to represent users' home-town check-in preference and geographical constraints in out-of-town check-in behaviors. Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM). Third, a non-linear mapping function, as well as a matrix factorization method, are employed to transfer users' home-town preference and estimate out-of-town POI's representation, respectively. Extensive experiments on real-world data sets validate the effectiveness of the TRAINOR framework. Moreover, the learned travel intention can deliver meaningful explanations for understanding a user's travel purposes.