{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:25:41Z","timestamp":1740104741537,"version":"3.37.3"},"reference-count":61,"publisher":"Wiley","license":[{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"],"award-info":[{"award-number":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National 14th Five Year Plan Key R&D Projects","award":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"],"award-info":[{"award-number":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"]}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"],"award-info":[{"award-number":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005089","name":"Beijing Municipal Natural Science Foundation","doi-asserted-by":"publisher","award":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"],"award-info":[{"award-number":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008521","name":"Beijing University of Civil Engineering and Architecture","doi-asserted-by":"publisher","award":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"],"award-info":[{"award-number":["61902016","62271036","2022YFB3305602-03","2018YFC0807806-2","4232021","J2023002","J2022005","PG2023086"]}],"id":[{"id":"10.13039\/501100008521","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2024,1,3]]},"abstract":"<jats:p>Besides data sparsity and cold start, recommender systems often face the problems of selection bias and exposure bias. These problems influence the accuracy of recommendations and easily lead to overrecommendations. This paper proposes a recommendation approach based on heterogeneous network and dynamic knowledge graph (HN-DKG). The main steps include (1) determining the implicit preferences of users according to user\u2019s cross-domain and cross-platform behaviors to form multimodal nodes and then building a heterogeneous knowledge graph; (2) Applying an improved multihead attention mechanism of the graph attention network (GAT) to realize the relationship enhancement of multimodal nodes and constructing a dynamic knowledge graph; and (3) Leveraging RippleNet to discover user\u2019s layered potential interests and rating candidate items. In which, some mechanisms, such as user seed clusters, propagation blocking, and random seed mechanisms, are designed to obtain more accurate and diverse recommendations. In this paper, the public datasets are used to evaluate the performance of algorithms, and the experimental results show that the proposed method has good performance in the effectiveness and diversity of recommendations. On the MovieLens-1M dataset, the proposed model is 18%, 9%, and 2% higher than KGAT on F1, NDCG@10, and AUC and 20%, 2%, and 0.9% higher than RippleNet, respectively. On the Amazon Book dataset, the proposed model is 12%, 3%, and 2.5% higher than NFM on F1, NDCG@10, and AUC and 0.8%, 2.3%, and 0.35% higher than RippleNet, respectively.<\/jats:p>","DOI":"10.1155\/2024\/4169402","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T23:50:18Z","timestamp":1704325818000},"page":"1-19","source":"Crossref","is-referenced-by-count":0,"title":["A Recommendation Approach Based on Heterogeneous Network and Dynamic Knowledge Graph"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3421-4387","authenticated-orcid":true,"given":"Shanshan","family":"Wan","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing 102616, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3320-7858","authenticated-orcid":true,"given":"Yuquan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9741-1927","authenticated-orcid":true,"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"People\u2019s Bank of China, Lanzhou, Gansu 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2352-6862","authenticated-orcid":true,"given":"Linhu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6228-6276","authenticated-orcid":true,"given":"Maozu","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing 102616, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107922"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-021-01618-9"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2022.102019"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3146443"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/1007\/1\/012144"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3125424"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00484-0"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2018.09.001"},{"issue":"6","key":"9","first-page":"137","article-title":"Research on recommendation algorithm of education platform for data sparsity","volume":"39","author":"Y. Wang","year":"2021","journal-title":"Journal of Jiamusi University (Natural Science Edition)"},{"article-title":"Research on interpretable recommendation method for sparse data","year":"2021","author":"B. Xu","key":"10"},{"article-title":"Multi-task feature learning for knowledge graph enhanced recommendation","author":"H. Wang","key":"11","doi-asserted-by":"crossref","DOI":"10.1145\/3308558.3313411"},{"first-page":"297","article-title":"Recurrent knowledge graph embedding for effective recommendation","author":"S. Zhu","key":"12"},{"first-page":"417","article-title":"Ripplenet: propagating user preferences on the knowledge graph for recommender systems","author":"H. Wang","key":"13"},{"first-page":"368","article-title":"Unbiased Ad click prediction for position-aware advertising systems","author":"B. Yuan","key":"14"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1145\/3130332.3130334"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.3390\/app13074389"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014456"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2018.2833443"},{"first-page":"2347","article-title":"Intentgc: a scalable graph convolution framework fusing heterogeneous information for recommendation","author":"J. Zhao","key":"19"},{"first-page":"2230","article-title":"Reinforced negative sampling for recommendation with exposure data","author":"J. Ding","key":"20"},{"first-page":"99","article-title":"Reinforced negative sampling over knowledge graph for recommendation","author":"X. Wang","key":"21"},{"key":"22","first-page":"6638","article-title":"Doubly robust joint learning for recommendation on data missing not at random","volume":"97","author":"X. Wang","year":"2019","journal-title":"International Conference on Machine Learning"},{"article-title":"Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning","author":"W. Zhang","key":"23","doi-asserted-by":"crossref","DOI":"10.1145\/3366423.3380037"},{"first-page":"425","article-title":"Dynamic heterogeneous graph embedding using hierarchical attentions","author":"L. Yang","key":"24"},{"key":"25","first-page":"577","article-title":"Attention-based models for speech recognition","volume":"1","author":"J. Chorowski","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"first-page":"577","article-title":"Hashtag recommendation using attention-based convolutional neural network","author":"Y. Gong","key":"26"},{"author":"P. Veli\u010dkovi\u0107","key":"27","article-title":"Graph attention networks"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-022-04369-8"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108038"},{"first-page":"2775","article-title":"Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning","author":"W. Zhang","key":"30"},{"first-page":"275","article-title":"Enhanced doubly robust learning for debiasing post-click conversion rate estimation","author":"S. Guo","key":"31"},{"first-page":"1595","article-title":"Meta structure: computing relevance in large heterogeneous information networks","author":"Z. Huang","key":"32"},{"author":"X. Yu","key":"33","article-title":"Collaborative filtering with entity similarity regularization in heterogeneous information networks"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2013.2297920"},{"article-title":"DKN: deep knowledge-aware network for news recommendation","author":"H. Wang","key":"35","doi-asserted-by":"crossref","DOI":"10.1145\/3178876.3186175"},{"key":"36","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-56212-4","volume-title":"Heterogeneous Information Network Analysis and Applications","author":"C. Shi","year":"2017"},{"issue":"10","key":"37","first-page":"2611","article-title":"Research on recommendation model for multi label system based on heterogeneous network","volume":"28","author":"Y. Wang","year":"2017","journal-title":"Journal of Software"},{"first-page":"1531","article-title":"Leveraging meta-path based context for top-n recommendation with a neural co-attention model","author":"B. Hu","key":"38"},{"first-page":"1276","article-title":"Incorporating heterogeneous information for personalized tag recommendation in social tagging systems","author":"W. Feng","key":"39"},{"first-page":"453","article-title":"Semantic path based personalized recommendation on weighted heterogeneous information networks","author":"C. Shi","key":"40"},{"first-page":"347","article-title":"Recommendation in heterogeneous information networks with implicit user feedback","author":"X. Yu","key":"41"},{"article-title":"Kgat: knowledge graph attention network for recommendation","author":"X. Wang","key":"42","doi-asserted-by":"crossref","DOI":"10.1145\/3292500.3330989"},{"article-title":"Research on relational metric social recommendation algorithm based on graph attention network","year":"2021","author":"X. Wang","key":"43"},{"key":"44","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2113\/1\/012085"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.1177\/15501329221082415"},{"article-title":"Research on file recommendation methods based on Cn RippleNet","year":"2022","author":"Y. Luo","key":"46"},{"article-title":"Research on improved RippleNet recommendation method","year":"2022","author":"C. Shi","key":"47"},{"key":"48","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2025\/1\/012011"},{"key":"49","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0251162"},{"key":"50","doi-asserted-by":"publisher","DOI":"10.3724\/sp.j.1087.2010.02618"},{"issue":"9","key":"51","first-page":"2177","article-title":"Application of prospective selection sampling algorithm in product recommendation","volume":"9","author":"J. Yang","year":"2005","journal-title":"Computer Applications"},{"key":"52","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101892"},{"first-page":"2464","article-title":"Cross-domain recommendation: an embedding and mapping approach","author":"T. Man","key":"53"},{"key":"54","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5329"},{"first-page":"353","article-title":"Collaborative knowledge base embedding for recommender systems","author":"F. Zhang","key":"55"},{"first-page":"355","article-title":"Neural factorization machines for sparse predictive analytics","author":"X. He","key":"56"},{"first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","author":"B. Antoine","key":"57"},{"author":"D. Bolya","key":"58","article-title":"Hydra attention: efficient attention with many heads"},{"article-title":"Research on social recommender system based on graph attention network","year":"2021","author":"M. Pan","key":"59"},{"key":"60","doi-asserted-by":"publisher","DOI":"10.1145\/1944339.1944341"},{"first-page":"109","article-title":"Rank and relevance in novelty and diversity metrics for recommender systems","author":"S. Vargas","key":"61"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2024\/4169402.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2024\/4169402.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2024\/4169402.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T23:50:27Z","timestamp":1704325827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/ijis\/2024\/4169402\/"}},"subtitle":[],"editor":[{"given":"Yu-an","family":"Tan","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,1,3]]},"references-count":61,"alternative-id":["4169402","4169402"],"URL":"https:\/\/doi.org\/10.1155\/2024\/4169402","relation":{},"ISSN":["1098-111X","0884-8173"],"issn-type":[{"type":"electronic","value":"1098-111X"},{"type":"print","value":"0884-8173"}],"subject":[],"published":{"date-parts":[[2024,1,3]]}}}