Citation Recommendation Using Heterogeneous Network Representation Learning and Attention Mechanism
[Objective]This study aims to expand the heterogeneous network in citation recommendations by including more nodes and relationships.It seeks to provide deep semantic representations and reveal how different relationships impact citation recommendations,ultimately improving the effectiveness of such recommendations.[Methods]By introducing semantic links,we constructed a heterogeneous network representation learning model incorporating an attention mechanism.This model generates deep semantic and structural representations,as well as similarity metrics for citation recommendations.We also conducted ablation experiments to explore the impact of different factors on citation recommendation.[Results]After introducing semantic links,the citation recommendation model's AUC improved by 0.012.With the addition of a dual-layer attention mechanism,there was a further improvement of 0.079 in AUC.Compared to the baseline model CR-HBNE,the AUC and AP improved by 0.185 and 0.204,respectively.[Limitations]Manual selection of relationship paths is inefficient,and evaluating the recommendation results based on only two metrics is relatively simplistic.[Conclusions]The proposed method fully utilizes the complex associations and deep semantic information among citations,effectively improving citation recommendation performance.
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