# Learning-based models for building user profiles for personalized information access

arXiv: https://arxiv.org/abs/2405.15791 · HTML: https://mlsanity.com/paper/2405.15791/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2024.

## Built on by (most cited first, 2 of 2)

- [Opinion Mining and Analysis Using Hybrid Deep Neural Networks](https://mlsanity.com/paper/2511.14796/) (2025)
- [Drift-Aware Multimodal User Representation Learning via Multi-Scale Temporal Modeling and Sparse Mixture-of-Experts](https://mlsanity.com/paper/2608.25773/) (2026)

## Abstract (arXiv)

This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data.