{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:18:25Z","timestamp":1743146305801,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031159305"},{"type":"electronic","value":"9783031159312"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-15931-2_44","type":"book-chapter","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T05:03:47Z","timestamp":1662440627000},"page":"532-543","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge Graph Bidirectional Interaction Graph Convolutional Network for\u00a0Recommendation"],"prefix":"10.1007","author":[{"given":"Zengqiang","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jijie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianqi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bangyu","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"44_CR1","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5\u20138, 2013, Lake Tahoe, Nevada, United States, pp. 2787\u20132795 (2013)"},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, H., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2016, Boston, MA, USA, 15 September 2016, pp. 7\u201310 (2016)","DOI":"10.1145\/2988450.2988454"},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"Diao, Q., Qiu, M., Wu, C., Smola, A.J., Jiang, J., Wang, C.: Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA - 24\u201327 August 2014, pp. 193\u2013202 (2014)","DOI":"10.1145\/2623330.2623758"},{"key":"44_CR4","doi-asserted-by":"crossref","unstructured":"He, X., Chua, T.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, 7\u201311 August 2017, pp. 355\u2013364 (2017)","DOI":"10.1145\/3077136.3080777"},{"key":"44_CR5","doi-asserted-by":"crossref","unstructured":"Juan, Y., Zhuang, Y., Chin, W., Lin, C.: Field-aware factorization machines for CTR prediction. In: Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15\u201319 September 2016, pp. 43\u201350 (2016)","DOI":"10.1145\/2959100.2959134"},{"key":"44_CR6","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25\u201330 January 2015, Austin, Texas, USA, pp. 2181\u20132187 (2015)"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14\u201317 December 2010, pp. 995\u20131000 (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines with libfm. ACM Trans. Intell. Syst. Technol. 3, 57:1\u201357:22 (2012)","DOI":"10.1145\/2168752.2168771"},{"key":"44_CR9","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, 18\u201321 June 2009, pp. 452\u2013461 (2009)"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Tang, X., Wang, T., Yang, H., Song, H.: AKUPM: attention-enhanced knowledge-aware user preference model for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, 4\u20138 August 2019, pp. 1891\u20131899 (2019)","DOI":"10.1145\/3292500.3330705"},{"key":"44_CR11","doi-asserted-by":"crossref","unstructured":"Wang, H., Lian, D., Ge, Y.: Binarized collaborative filtering with distilling graph convolutional network. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10\u201316 August 2019, pp. 4802\u20134808 (2019)","DOI":"10.24963\/ijcai.2019\/667"},{"key":"44_CR12","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Ripplenet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22\u201326 October 2018, pp. 417\u2013426 (2018)","DOI":"10.1145\/3269206.3271739"},{"key":"44_CR13","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, 4\u20138 August 2019, pp. 968\u2013977 (2019)","DOI":"10.1145\/3292500.3330836"},{"key":"44_CR14","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, F., Zhao, M., Li, W., Xie, X., Guo, M.: Multi-task feature learning for knowledge graph enhanced recommendation. In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, 13\u201317 May 2019, pp. 2000\u20132010 (2019)","DOI":"10.1145\/3308558.3313411"},{"key":"44_CR15","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, 13\u201317 May 2019, pp. 3307\u20133313 (2019)","DOI":"10.1145\/3308558.3313417"},{"key":"44_CR16","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29, 2724\u20132743 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"44_CR17","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y., Liu, M., Chua, T.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, 4\u20138 August 2019, pp. 950\u2013958 (2019)","DOI":"10.1145\/3292500.3330989"},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Lin, G., Tan, H., Chen, Q., Liu, X.: CKAN: collaborative knowledge-aware attentive network for recommender systems. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, 25\u201330 July 2020, pp. 219\u2013228 (2020)","DOI":"10.1145\/3397271.3401141"},{"key":"44_CR19","doi-asserted-by":"crossref","unstructured":"Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., Wang, M.: A neural influence diffusion model for social recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21\u201325 July 2019, pp. 235\u2013244 (2019)","DOI":"10.1145\/3331184.3331214"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Yu, X., et al.: Personalized entity recommendation: a heterogeneous information network approach. In: Seventh ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, 24\u201328 February 2014, pp. 283\u2013292 (2014)","DOI":"10.1145\/2556195.2556259"},{"key":"44_CR21","unstructured":"Yu, X., et al.: Recommendation in heterogeneous information networks with implicit user feedback. In: Seventh ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China, 12\u201316 October 2013, pp. 347\u2013350 (2013)"},{"key":"44_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13\u201317 August 2016, pp. 353\u2013362 (2016)","DOI":"10.1145\/2939672.2939673"},{"key":"44_CR23","doi-asserted-by":"crossref","unstructured":"Zheng, G., et al.: DRN: a deep reinforcement learning framework for news recommendation. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, 23\u201327 April 2018, pp. 167\u2013176 (2018)","DOI":"10.1145\/3178876.3185994"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, 19\u201323 August 2018, pp. 1059\u20131068 (2018)","DOI":"10.1145\/3219819.3219823"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15931-2_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T08:06:34Z","timestamp":1727942794000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15931-2_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031159305","9783031159312"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15931-2_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bristol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"561","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"255","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}