42,882 research outputs found
Link Prediction with Mutual Attention for Text-Attributed Networks
In this extended abstract, we present an algorithm that learns a similarity
measure between documents from the network topology of a structured corpus. We
leverage the Scaled Dot-Product Attention, a recently proposed attention
mechanism, to design a mutual attention mechanism between pairs of documents.
To train its parameters, we use the network links as supervision. We provide
preliminary experiment results with a citation dataset on two prediction tasks,
demonstrating the capacity of our model to learn a meaningful textual
similarity.Comment: Added missing referenc
Representation Learning for Recommender Systems with Application to the Scientific Literature
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
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