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KG has been applied in recommendation systems, exploiting graph neural networks (GNNs), but most existing recommendation models based on GNNs ignore the influence of node types and the loss of information during aggregation. In this paper, we propose a new model, named AMulti-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation (MAKR), that relieves the sparsity of the network and overcomes the limitation of information loss of the traditional GNN recommendation model. Specifically, we propose a new graph, named the Improved Collaborative Knowledge Graph (ICKG), that integrates user\u2013item interaction and a knowledge graph into a huge heterogeneous network, divides the nodes in the heterogeneous network into three categories\u2014users, items, and entities, and connects the edges according to the similarity between the users and items so as to enhance the high-order connectivity of the graph. In addition, we used attention mechanisms, the factorization machine (FM), and transformer (Trm) algorithms to aggregate messages from multi-granularity and different types to improve the representation ability of the model. The empirical results of three public benchmarks showed that MAKR outperformed state-of-the-art methods such as Neural FM, RippleNet, and KGAT.<\/jats:p>","DOI":"10.3390\/info13050229","type":"journal-article","created":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T05:44:07Z","timestamp":1651297447000},"page":"229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation"],"prefix":"10.3390","volume":"13","author":[{"given":"Xi","family":"Liu","sequence":"first","affiliation":[{"name":"College of Software, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Song","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Chongqing Research Institute, Jilin University, Chongqing 401123, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,29]]},"reference":[{"key":"ref_1","unstructured":"Warren, J., and Marz, N. 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