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Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach\u2014knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer\u2019s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.<\/jats:p>","DOI":"10.1007\/s11063-024-11475-6","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T18:02:27Z","timestamp":1707328947000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks"],"prefix":"10.1007","volume":"56","author":[{"given":"Guoshu","family":"Li","sequence":"first","affiliation":[]},{"given":"Li","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Sichang","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Song","sequence":"additional","affiliation":[]},{"given":"Yijun","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Shanqiang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"issue":"3","key":"11475_CR1","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3991\/ijet.v16i03.18851","volume":"16","author":"U Javed","year":"2021","unstructured":"Javed U, Shaukat K, Hameed IA, Iqbal F, Alam TM, Luo S (2021) A review of content-based and context-based recommendation systems. 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