# Representation Learning for Recommender Systems with Application to the Scientific Literature

## TL;DR

This paper explores representation learning for heterogeneous attributed networks in scientific literature, emphasizing the integration of textual data with graph structures to improve real-time recommendations and expert searches.

## Contribution

It highlights the importance of combining textual and network data in attributed network embeddings and addresses challenges in inferring representations for new documents.

## Key findings

- Identifies limitations of existing attributed network embedding methods.
- Proposes approaches to better integrate textual attributes with network data.
- Addresses the challenge of representing new documents without network information.

## Abstract

The scientific literature is a large information network linking various actors (laboratories, companies, institutions, etc.). The vast amount of data generated by this network constitutes a dynamic heterogeneous attributed network (HAN), in which new information is constantly produced and from which it is increasingly difficult to extract content of interest. In this article, I present my first thesis works in partnership with an industrial company, Digital Scientific Research Technology. This later offers a scientific watch tool, Peerus, addressing various issues, such as the real time recommendation of newly published papers or the search for active experts to start new collaborations. To tackle this diversity of applications, a common approach consists in learning representations of the nodes and attributes of this HAN and use them as features for a variety of recommendation tasks. However, most works on attributed network embedding pay too little attention to textual attributes and do not fully take advantage of recent natural language processing techniques. Moreover, proposed methods that jointly learn node and document representations do not provide a way to effectively infer representations for new documents for which network information is missing, which happens to be crucial in real time recommender systems. Finally, the interplay between textual and graph data in text-attributed heterogeneous networks remains an open research direction.