SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior Modeling
To address the limitations of existing knowledge graph-based recommendation algorithms, including insufficient utilization of semantic information and inadequate modeling of user behavior motivations, we propose SKGRec, a novel recommendation model that integrates knowledge graph and semantic featur...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/7/288 |
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| author | Siqi Xu Ziqian Yang Jing Xu Ping Feng |
| author_facet | Siqi Xu Ziqian Yang Jing Xu Ping Feng |
| author_sort | Siqi Xu |
| collection | DOAJ |
| description | To address the limitations of existing knowledge graph-based recommendation algorithms, including insufficient utilization of semantic information and inadequate modeling of user behavior motivations, we propose SKGRec, a novel recommendation model that integrates knowledge graph and semantic features. The model constructs a semantic interaction graph (USIG) of user behaviors and employs a self-attention mechanism and a ranked optimization loss function to mine user interactions in fine-grained semantic associations. A relationship-aware aggregation module is designed to dynamically integrate higher-order relational features in the knowledge graph through the attention scoring function. In addition, a multi-hop relational path inference mechanism is introduced to capture long-distance dependencies to improve the depth of user interest modeling. Experiments on the Amazon-Book and Last-FM datasets show that SKGRec significantly outperforms several state-of-the-art recommendation algorithms on the Recall@20 and NDCG@20 metrics. Comparison experiments validate the effectiveness of semantic analysis of user behavior and multi-hop path inference, while cold-start experiments further confirm the robustness of the model in sparse-data scenarios. This study provides a new optimization approach for knowledge graph and semantic-driven recommendation systems, enabling more accurate capture of user preferences and alleviating the problem of noise interference. |
| format | Article |
| id | doaj-art-f5c6f89e32ae4d92b0fa6ec3ca09635c |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-f5c6f89e32ae4d92b0fa6ec3ca09635c2025-08-20T03:36:19ZengMDPI AGComputers2073-431X2025-07-0114728810.3390/computers14070288SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior ModelingSiqi Xu0Ziqian Yang1Jing Xu2Ping Feng3College of Computer Science and Technology, Changchun University, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaMinistry of Education Key Laboratory of Intelligent Rehabilitation and Barrier-Free Access for the Disabled, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, Changchun 130022, ChinaTo address the limitations of existing knowledge graph-based recommendation algorithms, including insufficient utilization of semantic information and inadequate modeling of user behavior motivations, we propose SKGRec, a novel recommendation model that integrates knowledge graph and semantic features. The model constructs a semantic interaction graph (USIG) of user behaviors and employs a self-attention mechanism and a ranked optimization loss function to mine user interactions in fine-grained semantic associations. A relationship-aware aggregation module is designed to dynamically integrate higher-order relational features in the knowledge graph through the attention scoring function. In addition, a multi-hop relational path inference mechanism is introduced to capture long-distance dependencies to improve the depth of user interest modeling. Experiments on the Amazon-Book and Last-FM datasets show that SKGRec significantly outperforms several state-of-the-art recommendation algorithms on the Recall@20 and NDCG@20 metrics. Comparison experiments validate the effectiveness of semantic analysis of user behavior and multi-hop path inference, while cold-start experiments further confirm the robustness of the model in sparse-data scenarios. This study provides a new optimization approach for knowledge graph and semantic-driven recommendation systems, enabling more accurate capture of user preferences and alleviating the problem of noise interference.https://www.mdpi.com/2073-431X/14/7/288higher-order path reasoningknowledge graphrecommender systemsrelation awareness |
| spellingShingle | Siqi Xu Ziqian Yang Jing Xu Ping Feng SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior Modeling Computers higher-order path reasoning knowledge graph recommender systems relation awareness |
| title | SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior Modeling |
| title_full | SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior Modeling |
| title_fullStr | SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior Modeling |
| title_full_unstemmed | SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior Modeling |
| title_short | SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior Modeling |
| title_sort | skgrec a semantic enhanced knowledge graph fusion recommendation algorithm with multi hop reasoning and user behavior modeling |
| topic | higher-order path reasoning knowledge graph recommender systems relation awareness |
| url | https://www.mdpi.com/2073-431X/14/7/288 |
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