# Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating

arXiv: https://arxiv.org/abs/2110.06523 · HTML: https://mlsanity.com/paper/2110.06523/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [The Unfairness of Popularity Bias in Recommendation](https://mlsanity.com/paper/1907.13286/) (2019)
- [A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations](https://mlsanity.com/paper/2011.10187/) (2020)
- [Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence](https://mlsanity.com/paper/1809.10770/) (2018)
- [LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation](https://mlsanity.com/paper/1909.06667/) (2019)
- [Personalized Context-Aware Point of Interest Recommendation](https://mlsanity.com/paper/1806.05736/) (2018)
- [Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation](https://mlsanity.com/paper/1503.03650/) (2015)

## Abstract (arXiv)

POI recommendation is a key task in tourism information systems. However, in contrast to conventional point of interest (POI) recommender systems, the available data is extremely sparse; most tourist visit a few sightseeing spots once and most of these spots have no check-in data from new tourists. Most conventional systems rank sightseeing spots based on their popularity, reputations, and category-based similarities with users' preferences. They do not clarify what users can experience in these spots, which makes it difficult to meet diverse tourism needs. To this end, in this work, we propose a mechanism to recommend POIs to tourists. Our mechanism include two components: one is a probabilistic model that reveals the user behaviors in tourism; the other is a pseudo rating mechanism to handle the cold-start issue in POIs recommendations. We carried out extensive experiments with two datasets collected from Flickr. The experimental results demonstrate that our methods are superior to the state-of-the-art methods in both the recommendation performances (precision, recall and F-measure) and fairness. The experimental results also validate the robustness of the proposed methods, i.e., our methods can handle well the issue of data sparsity.