{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:04:59Z","timestamp":1765357499109,"version":"3.41.0"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Introducing a Knowledge Graph (KG) to facilitate a recommender system has become a tendency in recent years. Many existing methods leverage KGs to obtain side information of items to promote item representation learning for enhancing recommendation performance. However, they ignore that KGs also may contribute to better user representation learning. To solve this issue, we propose a novel algorithm, the KIGR (\n            <jats:underline>K<\/jats:underline>\n            nowledge-aware\n            <jats:underline>I<\/jats:underline>\n            nteraction\n            <jats:underline>G<\/jats:underline>\n            raph for\n            <jats:underline>R<\/jats:underline>\n            ecommendation), to mine user\u2013item interactions via KGs for assisting user representation learning. Specifically, a user\u2013item interaction is encoded by attentively summing up the relation embedding about the item in the KG. Then, an unsupervised learning method is used to group the user\u2013item interactions into different latent types. Further, a user\u2013item interaction graph is divided into several subgraphs, which is referred to as a Knowledge-aware Interaction Graph, making each subgraph only contain one latent type of interaction. Finally, user representation is the fusion of user interest embedding, which is learned on the knowledge-aware interaction graph, whereas item representation is learned on the KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the state-of-the-art algorithms.\n          <\/jats:p>","DOI":"10.1145\/3638065","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T22:10:57Z","timestamp":1703715057000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Mining User\u2013Item Interactions via Knowledge Graph for Recommendation"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8993-8940","authenticated-orcid":false,"given":"Shenghao","family":"Liu","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0667-5183","authenticated-orcid":false,"given":"Lingyun","family":"Lu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0312-4805","authenticated-orcid":false,"given":"Bang","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Translating embeddings for modeling multi-relational data","volume":"26","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems 26 (2013).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462900"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959131"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441762"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3501396"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018303"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080777"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219965"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16533"},{"key":"e_1_3_2_14_2","volume-title":"3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_15_2","first-page":"1","volume-title":"Proceedings of The International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of The International Conference on Learning Representations. 1\u201314."},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201920). 1748\u20131757.","DOI":"10.1145\/3394486.3403226"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3055147"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1082"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9491"},{"issue":"1","key":"e_1_3_2_20_2","first-page":"181","article-title":"Contextualized graph attention network for recommendation with item knowledge graph","volume":"35","author":"Liu Yong","year":"2021","unstructured":"Yong Liu, Susen Yang, Yonghui Xu, Chunyan Miao, Min Wu, and Juyong Zhang. 2021. Contextualized graph attention network for recommendation with item knowledge graph. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 181\u2013195.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313607"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403393"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498505"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462861"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2009916.2010002"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3424672"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.elerap.2021.101071"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105618"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240361"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271739"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186175"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330836"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313411"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313417"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450133"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512083"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482092"},{"key":"e_1_3_2_39_2","first-page":"4","volume-title":"Proceedings of International Joint Conference on Artificial Intelligent (IJCAI)","author":"Wang Zhigang","year":"2016","unstructured":"Zhigang Wang, Juanzi Li, Zhiyuan Liu, and Jie Tang. 2016. Text-enhanced representation learning for knowledge graph. In Proceedings of International Joint Conference on Artificial Intelligent (IJCAI). 4\u201317."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3298988"},{"key":"e_1_3_2_43_2","article-title":"Graph neural networks in recommender systems: A survey","author":"Wu Shiwen","year":"2020","unstructured":"Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2020. Graph neural networks in recommender systems: A survey. ACM Computing Surveys (CSUR) (2020).","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/547"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532009"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532009"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939673"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5701"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098063"},{"issue":"3","key":"e_1_3_2_50_2","first-page":"2641","article-title":"Hierarchical representation learning for attributed networks","volume":"35","author":"Zhao Shu","year":"2023","unstructured":"Shu Zhao, Ziwei Du, Jie Chen, Yanping Zhang, Jie Tang, and Philip S. Yu. 2023. Hierarchical representation learning for attributed networks. IEEE Transactions on Knowledge and Data Engineering 35, 3 (2023), 2641\u20132656.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2023.3296936"},{"issue":"2","key":"e_1_3_2_52_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3531267","article-title":"Time-aware path reasoning on knowledge graph for recommendation","volume":"41","author":"Zhao Yuyue","year":"2022","unstructured":"Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, and Haiyong Xie. 2022. Time-aware path reasoning on knowledge graph for recommendation. ACM Transactions on Information Systems 41, 2 (2022), 1\u201326.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106744"}],"container-title":["ACM Transactions on Recommender Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638065","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3638065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:35:53Z","timestamp":1750178153000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,22]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9,30]]}},"alternative-id":["10.1145\/3638065"],"URL":"https:\/\/doi.org\/10.1145\/3638065","relation":{},"ISSN":["2770-6699"],"issn-type":[{"type":"electronic","value":"2770-6699"}],"subject":[],"published":{"date-parts":[[2025,3,22]]},"assertion":[{"value":"2023-08-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}