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Graph-embedding-inspired article recommendation model
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AI
DOI：10.1016/j.eswa.2022.119100.png)
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Abstract
En 中文
Intelligent recommendation systems(IRSs) need to face the problems of sparse data and cold start. As one of the intelligent recommendation scenarios, article recommendation has rich and various article information, which is a significant difference from other scenarios. However, the existing methods of article recommender do not fully consider the article information or ignore the implicit correlation between users and articles. Therefore, this paper proposes a Graph-Embedding-inspired Article Recommendation Model (GE-ARM): we capture the User -Article correlation feature embeddings by constructing a Bipartite Graph with Resource Allocation (RA-BG); considering the different contributions of the Title, Abstract and Tag, Citation, design an Attention-based Dual-AutoEncoder, combining with Collaborative Filtering to fully capture the article feature embeddings; and update the correlation and article feature embeddings with Probabilistic Matrix Factorization to perform the final recommendation task. The experimental results highlight that GE-ARM outperforms the other known methods on four datasets.
Keywords:
Intelligent recommender
Bipartite graph
Resource allocation
Dual-AutoEncoder
Attention mechanism
Journal
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7.5
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2.9W
Citations:
10.2W
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CATA plus plus : A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles.png)
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