{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:35:25Z","timestamp":1769578525448,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Development Plan Project of Jilin Provincial Science and Technology Department","award":["2023JB405L07"],"award-info":[{"award-number":["2023JB405L07"]}]},{"name":"Science and Technology Development Plan Project of Jilin Provincial Science and Technology Department","award":["2024JBH05LA3"],"award-info":[{"award-number":["2024JBH05LA3"]}]},{"name":"University-Enterprise Joint Research Project of Jilin Provincial Department of Science and Technology","award":["2023JB405L07"],"award-info":[{"award-number":["2023JB405L07"]}]},{"name":"University-Enterprise Joint Research Project of Jilin Provincial Department of Science and Technology","award":["2024JBH05LA3"],"award-info":[{"award-number":["2024JBH05LA3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/computers14070288","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T11:12:34Z","timestamp":1752837154000},"page":"288","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SKGRec: A Semantic-Enhanced Knowledge Graph Fusion Recommendation Algorithm with Multi-Hop Reasoning and User Behavior Modeling"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6476-7093","authenticated-orcid":false,"given":"Siqi","family":"Xu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqian","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Intelligent Rehabilitation and Barrier-Free Access for the Disabled, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4865-1454","authenticated-orcid":false,"given":"Ping","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"},{"name":"Ministry of Education Key Laboratory of Intelligent Rehabilitation and Barrier-Free Access for the Disabled, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1023\/A:1009804230409","article-title":"E-commerce recommendation applications","volume":"5","author":"Schafer","year":"2001","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hu, B., Shi, C., Zhao, W.X., and Yu, P.S. (2018, January 19\u201323). Leveraging meta-path based context for top-n recommendation with a neural co-attention model. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219965"},{"key":"ref_3","unstructured":"Zhang, F., Yuan, N.J., Lian, D., Xie, X., and Ma, W.Y. (2018, January 19\u201323). Collaborative knowledge base embedding for recommender systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13071","DOI":"10.1007\/s10462-023-10465-9","article-title":"Knowledge graphs: Opportunities and challenges","volume":"56","author":"Peng","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"167","DOI":"10.3233\/SW-140134","article-title":"Dbpedia\u2013a large-scale, multilingual knowledge base extracted from wikipedia","volume":"6","author":"Lehmann","year":"2015","journal-title":"Semant. Web"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J. (2008, January 9\u201312). Freebase: A collaboratively created graph database for structuring human knowledge. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, BC, Canada.","DOI":"10.1145\/1376616.1376746"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"WordNet: A lexical database for English","volume":"38","author":"Miller","year":"1995","journal-title":"Commun. ACM"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Suchanek, F.M., Kasneci, G., and Weikum, G. (2007, January 8\u201312). Yago: A core of semantic knowledge. Proceedings of the 16th International Conference on World Wide Web, Banff, AB, Canada.","DOI":"10.1145\/1242572.1242667"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., and Zhu, X. (2015). Learning entity and relation embeddings for knowledge graph completion. AAAI Conf. Artif. Intell., 29.","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xin, X., He, X., Zhang, Y., Zhang, Y., and Jose, J. (2019, January 21\u201325). Relational collaborative filtering: Modeling multiple item relations for recom mendation. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Ser. SIGIR\u201919, Paris, France.","DOI":"10.1145\/3331184.3331188"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, X., Ren, X., Sun, Y., Sturt, B., Khandelwal, U., Gu, Q., Norick, B., and Han, J. (2013, January 12\u201316). Recommendation in heterogeneous information networks with implicit user feedback. Proceedings of the 7th ACM conference on Recommender systems, Hong Kong, China.","DOI":"10.1145\/2507157.2507230"},{"key":"ref_12","first-page":"5329","article-title":"Explainable reasoning over knowledge graphs for recommendation","volume":"33","author":"Wang","year":"2019","journal-title":"AAAI Conf. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhao, M., Xie, X., Li, W., and Guo, M. (2019, January 13\u201317). Knowledge graph convolutional networks for recommender systems. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313417"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y., Liu, M., and Chua, T.S. (2019, January 4\u20138). Kgat: Knowledge graph attention network for recommendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330989"},{"key":"ref_15","unstructured":"Sha, X., Sun, Z., and Zhang, J. (2019). Attentive knowledge graph embedding for personalized recommendation. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., and Guo, M. (2018, January 22\u201326). Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy.","DOI":"10.1145\/3269206.3271739"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tang, X., Wang, T., Yang, H., and Song, H. (2019, January 4\u20138). Akupm: Attention enhanced knowledge-aware user preference model for recom mendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330705"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The Graph Neural Network Model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_19","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, T., Wang, D., Yuan, Y., Liu, Z., He, X., and Chua, T.S. (2021, January 19\u201323). Learning intents behind interactions with knowledge graph for recommendation. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.","DOI":"10.1145\/3442381.3450133"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, R., and McAuley, J. (2016, January 11\u201315). Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. Proceedings of the 25th International Conference on World Wide Web, Montr\u00e9al, QC, Canada.","DOI":"10.1145\/2872427.2883037"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cantador, I., Brusilovsky, P., and Kuflik, T. (2011, January 23\u201327). Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011). Proceedings of the Fifth ACM Conference on Recommender Systems, Chicago, IL, USA.","DOI":"10.1145\/2043932.2044016"},{"key":"ref_23","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lin, G., Tan, H., Chen, Q., and Liu, X. (2020, January 25\u201330). CKAN: Collaborative knowledge-aware attentive network for recommender systems. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China.","DOI":"10.1145\/3397271.3401141"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, Y., Huang, C., Xia, L., and Li, C. (2022, January 11\u201315). Knowledge graph contrastive learning for recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain.","DOI":"10.1145\/3477495.3532009"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, Y., Huang, C., Xia, L., and Huang, C. (2023, January 6\u201310). Knowledge graph self-supervised rationalization for recommendation. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA.","DOI":"10.1145\/3580305.3599400"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/7\/288\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:12:04Z","timestamp":1760033524000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/7\/288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,18]]},"references-count":26,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["computers14070288"],"URL":"https:\/\/doi.org\/10.3390\/computers14070288","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,18]]}}}