# User Preferential Tour Recommendation Based on POI-Embedding Methods

arXiv: https://arxiv.org/abs/2103.02464 · HTML: https://mlsanity.com/paper/2103.02464/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Learning Points and Routes to Recommend Trajectories](https://mlsanity.com/paper/1608.07051/) (2016)

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

- [POIBERT: A Transformer-based Model for the Tour Recommendation Problem](https://mlsanity.com/paper/2212.13900/) (2022)
- [BTRec: BERT-Based Trajectory Recommendation for Personalized Tours](https://mlsanity.com/paper/2310.19886/) (2023)
- [Optimizing Group Utility in Itinerary Planning: A Strategic and Crowd-Aware Approach](https://mlsanity.com/paper/2304.08495/) (2023)
- [User Identification across Social Networking Sites using User Profiles and Posting Patterns](https://mlsanity.com/paper/2106.11815/) (2021)
- [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)

## Abstract (arXiv)

Tour itinerary planning and recommendation are challenging tasks for tourists in unfamiliar countries. Many tour recommenders only consider broad POI categories and do not align well with users' preferences and other locational constraints. We propose an algorithm to recommend personalized tours using POI-embedding methods, which provides a finer representation of POI types. Our recommendation algorithm will generate a sequence of POIs that optimizes time and locational constraints, as well as user's preferences based on past trajectories from similar tourists. Our tour recommendation algorithm is modelled as a word embedding model in natural language processing, coupled with an iterative algorithm for generating itineraries that satisfies time constraints. Using a Flickr dataset of 4 cities, preliminary experimental results show that our algorithm is able to recommend a relevant and accurate itinerary, based on measures of recall, precision and F1-scores.