{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:24:13Z","timestamp":1760145853000,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. Recommendation methods based on knowledge graphs can introduce user\u2013item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user\u2013item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces two main challenges: (1) in complex graph connection scenarios, how to adequately mine the self-information of each graph, and (2) how to merge interaction information from the two graphs while ensuring that user\u2013item interaction information predominates. Existing methods do not thoroughly explore the simultaneous mining of self-information from both graphs and effective interaction information, leading to the loss of valuable insights. Considering the success of contrastive learning in mining self-information and auxiliary information, this paper proposes a dual-graph contrastive learning recommendation method based on knowledge graphs (KGDC) to explore a more accurate representations of users and items in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC strengthens and represents the information of different connecting edges in both graphs, and extracts the existing information more fully. In interactive information learning, KGDC reinforces the interaction relationship between users and items in the external knowledge graph, realizing the leading role of the main task. We conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results.<\/jats:p>","DOI":"10.3390\/info15090534","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T12:54:42Z","timestamp":1725281682000},"page":"534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Knowledge-Aware Recommendation with Dual-Graph Contrastive Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5219-2126","authenticated-orcid":false,"given":"Jinchao","family":"Huang","sequence":"first","affiliation":[{"name":"China Unicom Research Institute, Beijing 100176, China"}]},{"given":"Zhipu","family":"Xie","sequence":"additional","affiliation":[{"name":"China Unicom Research Institute, Beijing 100176, China"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Unicom Research Institute, Beijing 100176, China"}]},{"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"China Unicom Research Institute, Beijing 100176, China"}]},{"given":"Chong","family":"Di","sequence":"additional","affiliation":[{"name":"Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5742-3766","authenticated-orcid":false,"given":"Runhe","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Computer & Information Sciences, Hosei University, Tokyo 184-8584, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., and Sargin, E. (2016, January 15\u201319). Deep neural networks for youtube recommendations. Proceedings of the 10th ACM Conference on Recommendation Systems, Boston, MA, USA.","key":"ref_1","DOI":"10.1145\/2959100.2959190"},{"doi-asserted-by":"crossref","unstructured":"Wang, J., Huang, P., Zhao, H., Zhang, Z., Zhao, B., and Lee, D.L. (2018, January 19\u201323). Billion-scale commodity embedding for e-commerce recommendation in alibaba. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","key":"ref_2","DOI":"10.1145\/3219819.3219869"},{"doi-asserted-by":"crossref","unstructured":"Zheng, G., Zhang, F., Zheng, Z., Xiang, Y., Yuan, N.J., Xie, X., and Li, Z. (2018, January 23\u201327). DRN: A deep reinforcement learning framework for news recommendation. Proceedings of the 2018 World Wide Web Conference, Lyon, France.","key":"ref_3","DOI":"10.1145\/3178876.3185994"},{"doi-asserted-by":"crossref","unstructured":"Lee, D., Kang, S., Ju, H., Park, C., and Yu, H. (2021, January 11\u201315). Bootstrapping user and item representations for one-class collaborative filtering. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual.","key":"ref_4","DOI":"10.1145\/3404835.3462935"},{"doi-asserted-by":"crossref","unstructured":"Wang, C., Yu, Y., Ma, W., Zhang, M., Chen, C., Liu, Y., and Ma, S. (2022, January 14\u201318). Towards representation alignment and uniformity in collaborative filtering. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","key":"ref_5","DOI":"10.1145\/3534678.3539253"},{"doi-asserted-by":"crossref","unstructured":"Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., and Xie, X. (2021, January 11\u201315). Self-supervised graph learning for recommendation. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual.","key":"ref_6","DOI":"10.1145\/3404835.3462862"},{"doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., and Nguyen, Q.V.H. (2022, January 11\u201315). Are graph augmentations necessary? Simple graph contrastive learning for recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain.","key":"ref_7","DOI":"10.1145\/3477495.3531937"},{"doi-asserted-by":"crossref","unstructured":"Ma, H., King, I., and Lyu, M.R. (2007, January 23\u201327). Effective missing data prediction for collaborative filtering. Proceedings of the 30th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands.","key":"ref_8","DOI":"10.1145\/1277741.1277751"},{"key":"ref_9","first-page":"4957","article-title":"Dropoutnet: Addressing cold start in recommendation systems","volume":"30","author":"Volkovs","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"doi-asserted-by":"crossref","unstructured":"Khawar, F., and Zhang, N.L. (2019, January 8\u201311). Modeling multidimensional user preferences for collaborative filtering. Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, China.","key":"ref_10","DOI":"10.1109\/ICDE.2019.00156"},{"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 recommendation systems. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy.","key":"ref_11","DOI":"10.1145\/3269206.3271739"},{"doi-asserted-by":"crossref","unstructured":"Wang, H., Zhao, M., Xie, X., Li, W., and Guo, M. (2019). Knowledge graph convolutional networks for recommendation systems. arXiv.","key":"ref_12","DOI":"10.1145\/3308558.3313417"},{"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.","key":"ref_13","DOI":"10.1145\/3292500.3330989"},{"doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, F., Zhang, M., Leskovec, J., Zhao, M., Li, W., and Wang, Z. (2019, January 4\u20138). Knowledge-aware graph neural networks with label smoothness regularization for recommendation systems. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","key":"ref_14","DOI":"10.1145\/3292500.3330836"},{"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 recommendation systems. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual.","key":"ref_15","DOI":"10.1145\/3397271.3401141"},{"doi-asserted-by":"crossref","unstructured":"Huang, J., Zhao, W.X., Dou, H., Wen, J.R., and Chang, E.Y. (2018, January 8\u201312). Improving sequential recommendation with knowledge-enhanced memory networks. Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA.","key":"ref_16","DOI":"10.1145\/3209978.3210017"},{"doi-asserted-by":"crossref","unstructured":"Zhang, F., Yuan, N.J., Lian, D., Xie, X., and Ma, W.Y. (2016, January 13\u201317). Collaborative knowledge base embedding for recommendation systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","key":"ref_17","DOI":"10.1145\/2939672.2939673"},{"unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Adv. Neural Inf. Process. Syst., 2787\u20132795.","key":"ref_18"},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., and Chen, Z. (2014, January 27\u201331). Knowledge graph embedding by translating on hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence, Qu\u00e9bec, QC, Canada.","key":"ref_19","DOI":"10.1609\/aaai.v28i1.8870"},{"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.","key":"ref_20","DOI":"10.1145\/3219819.3219965"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","article-title":"Heterogeneous information network embedding for recommendation","volume":"31","author":"Shi","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"unstructured":"Wang, X., Wang, D., Xu, C., He, X., Cao, Y., and Chua, T.S. (February, January 27). Explainable reasoning over knowledge graphs for recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.","key":"ref_22"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101071","DOI":"10.1016\/j.elerap.2021.101071","article-title":"Hierarchical attentive knowledge graph embedding for personalized recommendation","volume":"48","author":"Sha","year":"2021","journal-title":"Electron. Commer. Res. Appl."},{"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.","key":"ref_24","DOI":"10.1145\/3442381.3450133"},{"doi-asserted-by":"crossref","unstructured":"Cao, Y., Wang, X., He, X., Hu, Z., and Chua, T.S. (2019, January 13\u201317). Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","key":"ref_25","DOI":"10.1145\/3308558.3313705"},{"doi-asserted-by":"crossref","unstructured":"Yu, X., Ren, X., Sun, Y., Gu, Q., Sturt, B., Khandelwal, U., Norick, B., and Han, J. (2014, January 24\u201328). Personalized entity recommendation: A heterogeneous information network approach. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, New York, NY, USA.","key":"ref_26","DOI":"10.1145\/2556195.2556259"},{"doi-asserted-by":"crossref","unstructured":"Chen, Y., Yang, Y., Wang, Y., Bai, J., Song, X., and King, I. (2022, January 9\u201312). Attentive knowledge-aware graph convolutional networks with collaborative guidance for personalized recommendation. Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE), Virtual.","key":"ref_27","DOI":"10.1109\/ICDE53745.2022.00027"},{"doi-asserted-by":"crossref","unstructured":"Zou, D., Wei, W., Wang, Z., Mao, X.L., Zhu, F., Fang, R., and Chen, D. (2022, January 17\u201321). Improving knowledge-aware recommendation with multi-level interactive contrastive learning. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA.","key":"ref_28","DOI":"10.1145\/3511808.3557358"},{"unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A simple framework for contrastive learning of visual representations. Proceedings of the 37th International Conference on Machine Learning, Virtual.","key":"ref_29"},{"doi-asserted-by":"crossref","unstructured":"Yu, J., Yin, H., Gao, M., Xia, X., Zhang, X., and Viet Hung, N.Q. (2021, January 14\u201318). Socially-aware self-supervised tri-training for recommendation. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore.","key":"ref_30","DOI":"10.1145\/3447548.3467340"},{"doi-asserted-by":"crossref","unstructured":"Pan, Z., and Chen, H. (2021). Collaborative knowledge-enhanced recommendation with self-supervisions. Mathematics, 9.","key":"ref_31","DOI":"10.3390\/math9172129"},{"doi-asserted-by":"crossref","unstructured":"Yang, Y., Huang, C., Xia, L., and Li, C. (2021, 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.","key":"ref_32","DOI":"10.1145\/3477495.3532009"},{"unstructured":"Wang, H., Xu, Y., Yang, C., Shi, C., Li, X., Guo, N., and Liu, Z. (March, January 27). Knowledge-adaptive contrastive learning for recommendation. Proceedings of the sixteenth ACM International Conference on Web Search and Data Mining, Singapore.","key":"ref_33"},{"unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv.","key":"ref_34"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/9\/534\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:47:29Z","timestamp":1760111249000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/9\/534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,2]]},"references-count":34,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["info15090534"],"URL":"https:\/\/doi.org\/10.3390\/info15090534","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2024,9,2]]}}}