# Multi-view user representation learning for user matching without personal information

arXiv: https://arxiv.org/abs/2312.14533 · HTML: https://mlsanity.com/paper/2312.14533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Document Embedding with Paragraph Vectors](https://mlsanity.com/paper/1507.07998/) (2015)
- [Re-Identification and Growth Detection of Pulmonary Nodules without Image Registration Using 3D Siamese Neural Networks](https://mlsanity.com/paper/1912.10525/) (2019)
- [A Novel Random Forest Dissimilarity Measure for Multi-View Learning](https://mlsanity.com/paper/2007.02572/) (2020)
- [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)
- [Dynamic voting in multi-view learning for radiomics applications](https://mlsanity.com/paper/1806.07686/) (2018)
- [Cross-Device User Matching Based on Massive Browse Logs: The Runner-Up Solution for the 2016 CIKM Cup](https://mlsanity.com/paper/1610.03928/) (2016)
- [Cross Device Matching for Online Advertising with Neural Feature Ensembles : First Place Solution at CIKM Cup 2016](https://mlsanity.com/paper/1610.07119/) (2016)
- [Siamese Cookie Embedding Networks for Cross-Device User Matching](https://mlsanity.com/paper/1803.10450/) (2018)
- [Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016](https://mlsanity.com/paper/1612.07117/) (2016)
- [Destination similarity based on implicit user interest](https://mlsanity.com/paper/2102.06687/) (2021)

## Abstract (arXiv)

As the digitization of travel industry accelerates, analyzing and understanding travelers' behaviors becomes increasingly important. However, traveler data frequently exhibit high data sparsity due to the relatively low frequency of user interactions with travel providers. Compounding this effect the multiplication of devices, accounts and platforms while browsing travel products online also leads to data dispersion. To deal with these challenges, probabilistic traveler matching can be used. Most existing solutions for user matching are not suitable for traveler matching as a traveler's browsing history is typically short and URLs in the travel industry are very heterogeneous with many tokens. To deal with these challenges, we propose the similarity based multi-view information fusion to learn a better user representation from URLs by treating the URLs as multi-view data. The experimental results show that the proposed multi-view user representation learning can take advantage of the complementary information from different views, highlight the key information in URLs and perform significantly better than other representation learning solutions for the user matching task.