Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks

TL;DR

This paper presents HAI, an unsupervised heterogeneous graph learning model that predicts academic career moves and recommends institutions for early-career researchers, offering explainability through attention mechanisms.

Contribution

The paper introduces HAI, a novel deep learning model utilizing heterogeneous graph attention and mutual information for explainable academic institution recommendation.

Findings

HAI outperforms baseline methods in accuracy.

The model provides interpretable recommendations.

Experimental results confirm its effectiveness and efficiency.

Abstract

With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition and hence recommend suitable institutions for PhD graduates. Specifically, we design a deep learning model to predict the career move of early-career researchers and provide suggestions. The design is built on top of scholarly/academic networks, which contains abundant information about scientific collaboration among scholars and institutions. We construct a heterogeneous scholarly network to facilitate the exploring of the behavior of career moves and the recommendation of institutions for scholars. We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation. Moreover, we propose scholar attention and meta-path attention to discover the hidden relationships between several meta-paths. With these mechanisms, HAI provides ordered recommendations with explainability. We evaluate HAI upon a real-world dataset against baseline methods. Experimental results verify the effectiveness and efficiency of our approach.