{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T09:18:26Z","timestamp":1778923106156,"version":"3.51.4"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":320,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006247","name":"Anhui University of Science and Technology","doi-asserted-by":"publisher","award":["2024yjrc182"],"award-info":[{"award-number":["2024yjrc182"]}],"id":[{"id":"10.13039\/501100006247","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Networks"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.ijin.2025.11.007","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T03:09:58Z","timestamp":1763694598000},"page":"293-304","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["HCLDR: Heterogeneous Graph Convolutional Networks with Contrastive Learning for Diversity Recommendation"],"prefix":"10.1016","volume":"6","author":[{"given":"Hanwen","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiale","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunmei","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianmu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianjin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijin.2025.11.007_b1","series-title":"Proceedings of the 26th International Conference on World Wide Web","first-page":"173","article-title":"Neural collaborative filtering","author":"He","year":"2017"},{"key":"10.1016\/j.ijin.2025.11.007_b2","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"1059","article-title":"Deep interest network for click-through rate prediction","author":"Zhou","year":"2018"},{"key":"10.1016\/j.ijin.2025.11.007_b3","series-title":"Proceedings of the Eleventh International Conference on Learning Representations","article-title":"LightGCL: Simple yet effective graph contrastive learning for recommendation","author":"Cai","year":"2023"},{"key":"10.1016\/j.ijin.2025.11.007_b4","series-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"726","article-title":"Self-supervised graph learning for recommendation","author":"Wu","year":"2021"},{"key":"10.1016\/j.ijin.2025.11.007_b5","series-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"70","article-title":"Hypergraph contrastive collaborative filtering","author":"Xia","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b6","series-title":"The Filter Bubble: What the Internet is Hiding from You","author":"Pariser","year":"2011"},{"key":"10.1016\/j.ijin.2025.11.007_b7","series-title":"Going to extremes: How Like Minds Unite and Divide","author":"Sunstein","year":"2009"},{"key":"10.1016\/j.ijin.2025.11.007_b8","series-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","first-page":"4178","article-title":"An exploratory study of information cocoon on short form video platform","author":"Li","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b9","series-title":"Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"335","article-title":"The use of MMR, diversity-based reranking for reordering documents and producing summaries","author":"Carbonell","year":"1998"},{"key":"10.1016\/j.ijin.2025.11.007_b10","series-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","first-page":"2573","article-title":"ART (attractive recommendation tailor) how the diversity of product recommendations affects customer purchase preference in fashion industry?","author":"Kwon","year":"2020"},{"key":"10.1016\/j.ijin.2025.11.007_b11","series-title":"2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and CyberScience and Technology Congress","first-page":"68","article-title":"Learning to diversify recommendations based on matrix factorization","author":"Li","year":"2017"},{"key":"10.1016\/j.ijin.2025.11.007_b12","unstructured":"J. Wasilewski, N. Hurley, Incorporating diversity in a learning to rank recommender system, in: The Twenty-Ninth International Flairs Conference, 2016."},{"key":"10.1016\/j.ijin.2025.11.007_b13","series-title":"Fifteenth ACM Conference on Recommender Systems","first-page":"651","article-title":"Dynamic graph construction for improving diversity of recommendation","author":"Ye","year":"2021"},{"key":"10.1016\/j.ijin.2025.11.007_b14","series-title":"Proceedings of the Web Conference 2021","first-page":"401","article-title":"DGCN: Diversified recommendation with graph convolutional networks","author":"Zheng","year":"2021"},{"key":"10.1016\/j.ijin.2025.11.007_b15","series-title":"2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC\/DSS\/SmartCity\/DependSys)","first-page":"257","article-title":"FDRS: Federated diversified recommender system based on heterogeneous graph convolutional network","author":"Li","year":"2023"},{"key":"10.1016\/j.ijin.2025.11.007_b16","series-title":"Proceedings of the Eleventh ACM Conference on Recommender Systems","article-title":"Controlling popularity bias in learning-to-rank recommendation","author":"Abdollahpouri","year":"2017"},{"key":"10.1016\/j.ijin.2025.11.007_b17","series-title":"2022 IEEE 38th International Conference on Data Engineering","first-page":"1259","article-title":"Contrastive learning for sequential recommendation","author":"al","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106196","article-title":"Diversified service recommendation with high accuracy and efficiency","volume":"204","author":"Wang","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.ijin.2025.11.007_b19","series-title":"A survey of diversification techniques in search and recommendation","author":"Wu","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b20","series-title":"Proceedings of the 2008 ACM Conference on Recommender Systems","first-page":"123","article-title":"Avoiding monotony: improving the diversity of recommendation lists","author":"Zhang","year":"2008"},{"key":"10.1016\/j.ijin.2025.11.007_b21","series-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","first-page":"175","article-title":"Improving end-to-end sequential recommendations with intent-aware diversification","author":"Chen","year":"2020"},{"key":"10.1016\/j.ijin.2025.11.007_b22","unstructured":"L. Chen, G. Zhang, H. Zhou, Improving the diversity of top-N recommendation via determinantal point process, in: Large Scale Recommendation Systems Workshop, 2017."},{"key":"10.1016\/j.ijin.2025.11.007_b23","series-title":"Advances in Neural Information Processing Systems","first-page":"3149","article-title":"Expectation-maximization for learning determinantal point processes","volume":"vol. 27","author":"Gillenwater","year":"2014"},{"key":"10.1016\/j.ijin.2025.11.007_b24","doi-asserted-by":"crossref","unstructured":"S. Vargas, Novelty and diversity enhancement and evaluation in recommender systems and information retrieval, in: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, vol. 1281, 2014.","DOI":"10.1145\/2600428.2610382"},{"key":"10.1016\/j.ijin.2025.11.007_b25","unstructured":"Y. Zhang, H. Xiang, X. Xu, Z. Rui, X. Li, L. Qi, F. Dai, MEGAD: A Memory-Efficient Framework for Large-Scale Attributed Graph Anomaly Detection, in: Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25."},{"key":"10.1016\/j.ijin.2025.11.007_b26","series-title":"Graph neural networks for recommender systems: Challenges, methods, and directions","author":"Gao","year":"2021"},{"key":"10.1016\/j.ijin.2025.11.007_b27","series-title":"Graph convolutional matrix completion","author":"Berg","year":"2017"},{"key":"10.1016\/j.ijin.2025.11.007_b28","series-title":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"165","article-title":"Neural graph collaborative filtering","author":"Wang","year":"2019"},{"key":"10.1016\/j.ijin.2025.11.007_b29","series-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","first-page":"4264","article-title":"STAR-GCN: Stacked and reconstructed graph convolutional networks for recommender systems","author":"Zhang","year":"2019"},{"key":"10.1016\/j.ijin.2025.11.007_b30","series-title":"Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence","first-page":"27","article-title":"Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach","author":"Chen","year":"2020"},{"key":"10.1016\/j.ijin.2025.11.007_b31","series-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"639","article-title":"Lightgcn: Simplifying and powering graph convolution network for recommendation","author":"He","year":"2020"},{"key":"10.1016\/j.ijin.2025.11.007_b32","series-title":"Proceedings of the Web Conference","first-page":"593","article-title":"HGCF: Hyperbolic graph convolution networks for collaborative filtering","author":"Sun","year":"2021"},{"key":"10.1016\/j.ijin.2025.11.007_b33","series-title":"Proceedings of the ACM Web Conference","first-page":"17","article-title":"Graph-less collaborative filtering","author":"Xia","year":"2023"},{"issue":"6","key":"10.1016\/j.ijin.2025.11.007_b34","doi-asserted-by":"crossref","DOI":"10.1145\/3711858","article-title":"C2lRec: Causal contrastive learning for user cold-start recommendation with social variables","volume":"43","author":"Xu","year":"2025","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.ijin.2025.11.007_b35","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"9929","article-title":"Understanding contrastive representation learning through alignment and uniformity on the hypersphere","volume":"vol. 119","author":"Wang","year":"2020"},{"key":"10.1016\/j.ijin.2025.11.007_b36","series-title":"Proceedings of the ACM Web Conference","first-page":"2172","article-title":"Intent contrastive learning for sequential recommendation","author":"Chen","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b37","series-title":"Proceeding of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"1589","article-title":"CMCLRec: Cross-modal contrastive learning for user cold-start sequential recommendation","author":"Xu","year":"2024"},{"key":"10.1016\/j.ijin.2025.11.007_b38","unstructured":"F. Lu, X. Xu, H. Xiang, L. Qi, X. Zhou, F. Dai, W. Dou, CCLLMRec: Contrastive Learning with LLMs-based View Augmentation for Sequential Recommendation, in: Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25."},{"key":"10.1016\/j.ijin.2025.11.007_b39","series-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","first-page":"2817","article-title":"Improving knowledge-aware recommendation with multi-level interactive contrastive learning","author":"Zou","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b40","series-title":"Proceedings of the ACM Web Conference","first-page":"2216","article-title":"Re4: Learning to re-contrast, re-attend, reconstruct for multi-interest recommendation","author":"Zhang","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b41","series-title":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","first-page":"1120","article-title":"Contrastive meta learning with behavior multiplicity for recommendation","author":"Wei","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b42","series-title":"KDD \u201922: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"1816","article-title":"Towards representation alignment and uniformity in collaborative filtering","author":"Wang","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b43","series-title":"Proceedings of the ACM Web Conference","first-page":"2320","article-title":"Improving graph collaborative filtering with neighborhood-enriched contrastive learning","author":"Lin","year":"2022"},{"key":"10.1016\/j.ijin.2025.11.007_b44","series-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"1294","article-title":"Are graph augmentations necessary?: Simple graph contrastive learning for recommendation","author":"Yu","year":"2022"},{"issue":"2","key":"10.1016\/j.ijin.2025.11.007_b45","first-page":"913","article-title":"XSimGCL: Towards extremely simple graph contrastive learning for recommendation","volume":"36","author":"Yu","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.ijin.2025.11.007_b46","first-page":"1637","article-title":"Interpretable and efficient heterogeneous graph convolutional network","volume":"35","author":"Yang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.ijin.2025.11.007_b47","series-title":"Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence","first-page":"452","article-title":"BPR: Bayesian personalized ranking from implicit feedback","author":"Rendle","year":"2009"},{"key":"10.1016\/j.ijin.2025.11.007_b48","series-title":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","first-page":"661","article-title":"Dgrec: Graph neural network for recommendation with diversified embedding generation","author":"Yang","year":"2023"}],"container-title":["International Journal of Intelligent Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666603025000259?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666603025000259?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T08:53:59Z","timestamp":1778921639000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666603025000259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":48,"alternative-id":["S2666603025000259"],"URL":"https:\/\/doi.org\/10.1016\/j.ijin.2025.11.007","relation":{},"ISSN":["2666-6030"],"issn-type":[{"value":"2666-6030","type":"print"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"HCLDR: Heterogeneous Graph Convolutional Networks with Contrastive Learning for Diversity Recommendation","name":"articletitle","label":"Article Title"},{"value":"International Journal of Intelligent Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijin.2025.11.007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.","name":"copyright","label":"Copyright"}]}}