{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:40:57Z","timestamp":1790358057735,"version":"4.1.0"},"reference-count":65,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"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. Inf. Syst."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>\n                    Incorporating knowledge graphs (KGs) into recommender systems to provide explainable recommendation has attracted much attention recently. The multi-hop paths in KGs can provide auxiliary facts for improving recommendation performance as well as explainability. However, existing studies may suffer from two major challenges: error propagation and weak explainability. Considering all paths between every user-item pair might involve irrelevant ones, which leads to error propagation of user preferences. Defining meta-paths might alleviate the error propagation, but the recommendation performance would heavily depend on the pre-defined meta-paths. Some recent methods based on graph convolution network (GCN) achieve better recommendation performance, but fail to provide explainability. To tackle the above problems, we propose a novel method named\n                    <jats:bold>K<\/jats:bold>\n                    nowledge-aware\n                    <jats:bold>R<\/jats:bold>\n                    easoning with\n                    <jats:bold>G<\/jats:bold>\n                    raph\n                    <jats:bold>C<\/jats:bold>\n                    onvolution\n                    <jats:bold>N<\/jats:bold>\n                    etwork (KR-GCN). Specifically, to alleviate the effect of error propagation, we design a transition-based method to determine the triple-level scores and utilize nucleus sampling to select triples within the paths between every user-item pair adaptively. To improve the recommendation performance and guarantee the diversity of explanations, user-item interactions and knowledge graphs are integrated into a heterogeneous graph, which is performed with the graph convolution network. A path-level self-attention mechanism is adopted to discriminate the contributions of different selected paths and predict the interaction probability, which improves the relevance of the final explanation. Extensive experiments conducted on three real-world datasets show that KR-GCN consistently outperforms several state-of-the-art baselines. And human evaluation proves the superiority of KR-GCN on explainability.\n                  <\/jats:p>","DOI":"10.1145\/3511019","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T17:29:06Z","timestamp":1647365346000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":106,"title":["KR-GCN: Knowledge-Aware Reasoning with Graph Convolution Network for Explainable Recommendation"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9114-2148","authenticated-orcid":false,"given":"Ting","family":"Ma","sequence":"first","affiliation":[{"name":"School of Cyber Security, University of Chinese Academy of Sciences; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0517-1592","authenticated-orcid":false,"given":"Longtao","family":"Huang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0155-1683","authenticated-orcid":false,"given":"Qianqian","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7170-3809","authenticated-orcid":false,"given":"Songlin","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Cyber Security, University of Chinese Academy of Sciences; Institute ofInformation Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"issue":"9","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3390\/a11090137","article-title":"Learning heterogeneous knowledge base embeddings for explainable recommendation","volume":"11","author":"Ai Qingyao","year":"2018","unstructured":"Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms 11, 9 (2018), 137.","journal-title":"Algorithms"},{"key":"e_1_3_1_3_2","first-page":"1247","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD","author":"Bollacker Kurt","year":"2008","unstructured":"Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD. 1247\u20131250."},{"key":"e_1_3_1_4_2","first-page":"2787","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 26: The 27th Annual Conference on Neural Information Processing Systems","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. In Proceedings of the Advances in Neural Information Processing Systems 26: The 27th Annual Conference on Neural Information Processing Systems. 2787\u20132795."},{"key":"e_1_3_1_5_2","first-page":"151","volume-title":"Proceedings of the 2019 World Wide Web Conference on World Wide Web,WWW","author":"Cao Yixin","year":"2019","unstructured":"Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In Proceedings of the 2019 World Wide Web Conference on World Wide Web,WWW. 151\u2013161."},{"key":"e_1_3_1_6_2","first-page":"189","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR","author":"Chen Chong","year":"2020","unstructured":"Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2020. Jointly non-sampling learning for knowledge graph enhanced recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR. 189\u2013198."},{"key":"e_1_3_1_7_2","first-page":"1056","volume-title":"Proceedings of the WSDM\u201921, The Fourteenth ACM International Conference on Web Search and Data Mining, 2021","author":"Chen Hongxu","year":"2021","unstructured":"Hongxu Chen, Yicong Li, Xiangguo Sun, Guandong Xu, and Hongzhi Yin. 2021. Temporal meta-path guided explainable recommendation. In Proceedings of the WSDM\u201921, The Fourteenth ACM International Conference on Web Search and Data Mining, 2021. ACM, 1056\u20131064."},{"issue":"1","key":"e_1_3_1_8_2","first-page":"8:1\u20138:22","article-title":"Neural feature-aware recommendation with signed hypergraph convolutional network","volume":"39","author":"Chen Xu","year":"2020","unstructured":"Xu Chen, Kun Xiong, Yongfeng Zhang, Long Xia, Dawei Yin, and Jimmy Xiangji Huang. 2020. Neural feature-aware recommendation with signed hypergraph convolutional network. ACM Transactions on Information Systems 39, 1 (2020), 8:1\u20138:22.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_9_2","first-page":"2137","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019","author":"Chen Zhongxia","year":"2019","unstructured":"Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, and Enhong Chen. 2019. Co-Attentive multi-task learning for explainable recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, Sarit Kraus (Ed.). 2137\u20132143."},{"issue":"2","key":"e_1_3_1_10_2","first-page":"16:1\u201316:28","article-title":"MMALFM: Explainable recommendation by leveraging reviews and images","volume":"37","author":"Cheng Zhiyong","year":"2019","unstructured":"Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose Catherine Kanjirathinkal, and Mohan S. Kankanhalli. 2019. MMALFM: Explainable recommendation by leveraging reviews and images. ACM Transactions on Information Systems 37, 2 (2019), 16:1\u201316:28.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_11_2","first-page":"2478","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD","author":"Fan Shaohua","year":"2019","unstructured":"Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, and Yongliang Li. 2019. Metapath-guided heterogeneous graph neural network for intent recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD. 2478\u20132486."},{"key":"e_1_3_1_12_2","first-page":"417","volume-title":"Proceedings of the World Wide Web Conference, WWW 2019","author":"Fan Wenqi","year":"2019","unstructured":"Wenqi Fan, Yao Ma, Qing Li, Yuan He, Yihong Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In Proceedings of the World Wide Web Conference, WWW 2019. 417\u2013426."},{"key":"e_1_3_1_13_2","first-page":"69","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020","author":"Fu Zuohui","year":"2020","unstructured":"Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, and Gerard de Melo. 2020. Fairness-Aware explainable recommendation over knowledge graphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020. 69\u201378."},{"key":"e_1_3_1_14_2","first-page":"3622","volume-title":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence, AAAI 2019","author":"Gao Jingyue","year":"2019","unstructured":"Jingyue Gao, Xiting Wang, Yasha Wang, and Xing Xie. 2019. Explainable Recommendation through Attentive Multi-View Learning. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, AAAI 2019. 3622\u20133629."},{"key":"e_1_3_1_15_2","series-title":"Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010","first-page":"249","volume":"9","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010(JMLR Proceedings, Vol. 9). 249\u2013256."},{"key":"e_1_3_1_16_2","first-page":"355","volume-title":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR. 355\u2013364."},{"key":"e_1_3_1_17_2","first-page":"639","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR","author":"He Xiangnan","year":"2020","unstructured":"Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR. 639\u2013648."},{"key":"e_1_3_1_18_2","first-page":"173","volume-title":"Proceedings of the 26th International Conference on World Wide Web, WWW","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web, WWW. 173\u2013182."},{"key":"e_1_3_1_19_2","volume-title":"Proceedings of the 8th International Conference on Learning Representations, ICLR 2020","author":"Holtzman Ari","year":"2020","unstructured":"Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The curious case of neural text degeneration. In Proceedings of the 8th International Conference on Learning Representations, ICLR 2020."},{"key":"e_1_3_1_20_2","first-page":"1531","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD","author":"Hu Binbin","year":"2018","unstructured":"Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu. 2018. Leveraging meta-path based Context for Top-N recommendation with a neural co-attention model. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD. 1531\u20131540."},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1145\/3343031.3350893","volume-title":"Proceedings of the 27th ACM International Conference on Multimedia, MM 2019","author":"Huang Xiaowen","year":"2019","unstructured":"Xiaowen Huang, Quan Fang, Shengsheng Qian, Jitao Sang, Yan Li, and Changsheng Xu. 2019. Explainable interaction-driven user modeling over knowledge graph for sequential recommendation. In Proceedings of the 27th ACM International Conference on Multimedia, MM 2019. 548\u2013556."},{"key":"e_1_3_1_22_2","unstructured":"Kingma Diederik and Ba Jimmy. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3th International Conference on Learning Representations (ICLR\u201915) ."},{"key":"e_1_3_1_23_2","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. CoRR abs\/1611.07308 (2016)."},{"key":"e_1_3_1_24_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations, ICLR","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 5th International Conference on Learning Representations, ICLR."},{"key":"e_1_3_1_25_2","first-page":"6072","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems, NeurIPS 2018","author":"Li Chongxuan","year":"2018","unstructured":"Chongxuan Li, Max Welling, Jun Zhu, and Bo Zhang. 2018. Graphical generative adversarial networks. In Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems, NeurIPS 2018. 6072\u20136083."},{"key":"e_1_3_1_26_2","first-page":"509","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019","author":"Li Mengmeng","year":"2019","unstructured":"Mengmeng Li, Tian Gan, Meng Liu, Zhiyong Cheng, Jianhua Yin, and Liqiang Nie. 2019. Long-tail hashtag recommendation for micro-videos with graph convolutional network. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019. 509\u2013518."},{"key":"e_1_3_1_27_2","first-page":"122","volume-title":"Proceedings of the WWW\u201920: The Web Conference, 2020","author":"Li Xueqi","year":"2020","unstructured":"Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang, and Kenli Li. 2020. Directional and explainable serendipity recommendation. In Proceedings of the WWW\u201920: The Web Conference, 2020. 122\u2013132."},{"key":"e_1_3_1_28_2","first-page":"2181","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence Learning","author":"Lin Yankai","year":"2015","unstructured":"Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the 29th AAAI Conference on Artificial Intelligence Learning. 2181\u20132187."},{"key":"e_1_3_1_29_2","first-page":"1055","volume-title":"Proceedings of the KDD\u201921: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Liu Danyang","year":"2021","unstructured":"Danyang Liu, Jianxun Lian, Zheng Liu, Xiting Wang, Guangzhong Sun, and Xing Xie. 2021. Reinforced anchor knowledge graph generation for news recommendation reasoning. In Proceedings of the KDD\u201921: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1055\u20131065."},{"key":"e_1_3_1_30_2","first-page":"1210","volume-title":"Proceedings of the World Wide Web Conference, WWW 2019","author":"Ma Weizhi","year":"2019","unstructured":"Weizhi Ma, Min Zhang, Yue Cao, Woojeong Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, and Xiang Ren. 2019. Jointly learning explainable rules for recommendation with knowledge graph. In Proceedings of the World Wide Web Conference, WWW 2019. 1210\u20131221."},{"issue":"1","key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1145\/375360.375365","article-title":"A guided tour to approximate string matching","volume":"33","author":"Navarro Gonzalo","year":"2001","unstructured":"Gonzalo Navarro. 2001. A guided tour to approximate string matching. ACM Computing Surveys 33, 1 (2001), 31\u201388.","journal-title":"ACM Computing Surveys"},{"issue":"3","key":"e_1_3_1_32_2","first-page":"22:1\u201322:23","article-title":"Exploiting cross-session information for session-based recommendation with graph neural networks","volume":"38","author":"Qiu Ruihong","year":"2020","unstructured":"Ruihong Qiu, Zi Huang, Jingjing Li, and Hongzhi Yin. 2020. Exploiting cross-session information for session-based recommendation with graph neural networks. ACM Transactions on Information Systems 38, 3 (2020), 22:1\u201322:23.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_33_2","unstructured":"Yanru Qu Ting Bai Weinan Zhang Jian-Yun Nie and Jian Tang. 2019. An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation. CoRR abs\/1908.04032 (2019)."},{"key":"e_1_3_1_34_2","volume-title":"Proceedings of the Unpublished Manuscript","author":"Radford Alec","year":"2019","unstructured":"Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. In Proceedings of the Unpublished Manuscript. Retrieved from https:\/\/d4mucfpksywv.cloudfront.net\/better-language-models\/language_models_are_unsupervised_multitask_learners.pdf."},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1145\/3437963.3441746","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Ragesh Rahul","year":"2021","unstructured":"Rahul Ragesh, Sundararajan Sellamanickam, Arun Iyer, Ramakrishna Bairi, and Vijay Lingam. 2021. HeteGCN: Heterogeneous graph convolutional networks for text classification. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 860\u2013868."},{"key":"e_1_3_1_36_2","first-page":"452","volume-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452\u2013461."},{"key":"e_1_3_1_37_2","unstructured":"Weiping Song Zhijian Duan Ziqing Yang Hao Zhu Ming Zhang and Jian Tang. 2019. Explainable knowledge graph-based recommendation via deep reinforcement learning. CoRR abs\/1906.09506 (2019)."},{"key":"e_1_3_1_38_2","first-page":"555","volume-title":"Proceedings of the 12th ACM International Conference on Web Search and Data Mining, WSDM 2019","author":"Song Weiping","year":"2019","unstructured":"Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. 2019. Session-based social recommendation via dynamic graph attention networks. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining, WSDM 2019. 555\u2013563."},{"key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1145\/3240323.3240361","volume-title":"Proceedings of the 12th ACM Conference on Recommender Systems","author":"Sun Zhu","year":"2018","unstructured":"Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, and Chi Xu. 2018. Recurrent knowledge graph embedding for effective recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems. 297\u2013305."},{"key":"e_1_3_1_40_2","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1145\/3292500.3330705","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019","author":"Tang Xiaoli","year":"2019","unstructured":"Xiaoli Tang, Tengyun Wang, Haizhi Yang, and Hengjie Song. 2019. AKUPM: Attention-Enhanced knowledge-aware user preference model for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019. 1891\u20131899."},{"key":"e_1_3_1_41_2","first-page":"2071","volume-title":"Proceedings of the 33th International Conference on Machine Learning","year":"2016","unstructured":"Trouillon, Johannes Welb, Sebastian Riedel, Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In Proceedings of the 33th International Conference on Machine Learning. 2071\u20132080."},{"key":"e_1_3_1_42_2","volume-title":"Proceedings of the 6th International Conference on Learning Representations, ICLR 2018","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations, ICLR 2018. OpenReview.net."},{"key":"e_1_3_1_43_2","first-page":"417","volume-title":"Proceedings of the 27th ACM on Conference on Information and Knowledge Management, CIKM","author":"Wang Hongwei","year":"2018","unstructured":"Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. RippleNet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM on Conference on Information and Knowledge Management, CIKM. 417\u2013426."},{"key":"e_1_3_1_44_2","volume-title":"Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW","author":"Wang Hongwei","unstructured":"Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. [n.d.]. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW."},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1145\/3292500.3330836","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019","author":"Wang Hongwei","year":"2019","unstructured":"Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. 2019. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019. 968\u2013977."},{"key":"e_1_3_1_46_2","first-page":"3307","volume-title":"Proceedings of the 2019 World Wide Web Conference on World Wide Web, WWW","author":"Wang Hongwei","year":"2019","unstructured":"Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In Proceedings of the 2019 World Wide Web Conference on World Wide Web, WWW. 3307\u20133313."},{"key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1145\/3292500.3330989","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD","author":"Wang Xiang","year":"2019","unstructured":"Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. KGAT: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD. 950\u2013958."},{"key":"e_1_3_1_48_2","first-page":"5329","volume-title":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence, AAAI","author":"Wang Xiang","year":"2019","unstructured":"Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. 2019. Explainable reasoning over knowledge graphs for recommendation. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, AAAI. 5329\u20135336."},{"key":"e_1_3_1_49_2","first-page":"1112","volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence","author":"Wang Zhen","year":"2014","unstructured":"Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the 28th AAAI Conference on Artificial Intelligence. 1112\u20131119."},{"key":"e_1_3_1_50_2","first-page":"2091","volume-title":"Proceedings of the World Wide Web Conference, WWW 2019","author":"Wu Qitian","year":"2019","unstructured":"Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. 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In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019. 125\u2013134."},{"issue":"3","key":"e_1_3_1_53_2","first-page":"33:1\u201333:25","article-title":"Deep item-based collaborative filtering for Top-N recommendation","volume":"37","author":"Xue Feng","year":"2019","unstructured":"Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, and Richang Hong. 2019. Deep item-based collaborative filtering for Top-N recommendation. ACM Transactions on Information Systems 37, 3 (2019), 33:1\u201333:25.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_54_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations, ICLR","author":"Yang Bishan","year":"2015","unstructured":"Bishan Yang, Wen tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding entities and relations for learning and inference in knowledge bases. 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In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020. 1909\u20131912."},{"key":"e_1_3_1_57_2","first-page":"2829","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Yuchen Lin Bill","year":"2019","unstructured":"Lin Bill Yuchen, Chen Xinyue, Chen Jamin, and Ren Xiang. 2019. KagNet: Knowledge-Aware graph networks for commonsense reasoning. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2829\u20132839."},{"issue":"1","key":"e_1_3_1_58_2","first-page":"7:1\u20137:22","article-title":"Next-Item recommendation via collaborative filtering with bidirectional item similarity","volume":"38","author":"Zeng Zijie","year":"2020","unstructured":"Zijie Zeng, Jing Lin, Lin Li, Weike Pan, and Zhong Ming. 2020. Next-Item recommendation via collaborative filtering with bidirectional item similarity. 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Meta-Graph based recommendation fusion over heterogeneous information networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD. 635\u2013644."},{"key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1145\/3292500.3330686","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019","author":"Zhao Jun","year":"2019","unstructured":"Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, and Xiaofei He. 2019. IntentGC: A scalable graph convolution framework fusing heterogeneous information for recommendation. 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