# Destination similarity based on implicit user interest

arXiv: https://arxiv.org/abs/2102.06687 · HTML: https://mlsanity.com/paper/2102.06687/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images](https://mlsanity.com/paper/1803.11241/) (2018)

## Built on by (most cited first, 1 of 1)

- [Multi-view user representation learning for user matching without personal information](https://mlsanity.com/paper/2312.14533/) (2023)

## Abstract (arXiv)

With the digitization of travel industry, it is more and more important to understand users from their online behaviors. However, online travel industry data are more challenging to analyze due to extra sparseness, dispersed user history actions, fast change of user interest and lack of direct or indirect feedbacks. In this work, a new similarity method is proposed to measure the destination similarity in terms of implicit user interest. By comparing the proposed method to several other widely used similarity measures in recommender systems, the proposed method achieves a significant improvement on travel data. Key words: Destination similarity, Travel industry, Recommender System, Implicit user interest