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

TL;DR

This paper explores deep learning models to create personalized user profiles for information access, emphasizing user involvement and advanced text representations to improve relevance.

Contribution

It introduces a user-centered approach employing deep learning to better model document content and user preferences for personalized information retrieval.

Findings

Deep learning models effectively capture complex textual patterns.

User involvement enhances relevance of personalized information.

Hierarchical and attention-based models improve document-query matching.

Abstract

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.