MIARec: Mutual-influence-aware Heterogeneous Network Embedding for Scientific Paper Recommendation
This work improves recommendation accuracy for scholars by better modeling influence in scholarly networks, though it is incremental as it builds on existing graph-based approaches.
The paper tackles the problem of scientific paper recommendation by addressing the oversight of asymmetric academic influence in graph-based methods, proposing the MIARec model that incorporates mutual influence and multi-channel aggregation, and shows it outperforms baselines on real-world datasets across three evaluation metrics.
With the rapid expansion of scientific literature, scholars increasingly demand precise and high-quality paper recommendations. Among various recommendation methodologies, graph-based approaches have garnered attention by effectively exploiting the structural characteristics inherent in scholarly networks. However, these methods often overlook the asymmetric academic influence that is prevalent in scholarly networks when learning graph representations. To address this limitation, this study proposes the Mutual-Influence-Aware Recommendation (MIARec) model, which employs a gravity-based approach to measure the mutual academic influence between scholars and incorporates this influence into the feature aggregation process during message propagation in graph representation learning. Additionally, the model utilizes a multi-channel aggregation method to capture both individual embeddings of distinct single relational sub-networks and their interdependent embeddings, thereby enabling a more comprehensive understanding of the heterogeneous scholarly network. Extensive experiments conducted on real-world datasets demonstrate that the MIARec model outperforms baseline models across three primary evaluation metrics, indicating its effectiveness in scientific paper recommendation tasks.