{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:50:20Z","timestamp":1742968220771,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811664700"},{"type":"electronic","value":"9789811664717"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-6471-7_19","type":"book-chapter","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T23:03:34Z","timestamp":1635375814000},"page":"253-264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-stage Knowledge Propagation Network for Recommendation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4962-9734","authenticated-orcid":false,"given":"Feng","family":"Xue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1727-7108","authenticated-orcid":false,"given":"Wenjie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4522-5401","authenticated-orcid":false,"given":"Zikun","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6789-1811","authenticated-orcid":false,"given":"Kang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173\u2013182. WWW 2017, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https:\/\/doi.org\/10.1145\/3038912.3052569","key":"19_CR1","DOI":"10.1145\/3038912.3052569"},{"doi-asserted-by":"publisher","unstructured":"Xue, F., He, X., Wang, X., Xu, J., Liu, K., Hong, R.: Deep item-based collaborative filtering for top-n recommendation. ACM Trans. Inf. Syst. 37(3), 1\u201325 (2019). https:\/\/doi.org\/10.1145\/3314578","key":"19_CR2","DOI":"10.1145\/3314578"},{"doi-asserted-by":"publisher","unstructured":"Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM (2016). https:\/\/doi.org\/10.1145\/2988450.2988454","key":"19_CR3","DOI":"10.1145\/2988450.2988454"},{"doi-asserted-by":"publisher","unstructured":"Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee (2015). https:\/\/doi.org\/10.1145\/2736277.2741667","key":"19_CR4","DOI":"10.1145\/2736277.2741667"},{"doi-asserted-by":"publisher","unstructured":"Catherine, R., Cohen, W.: Personalized recommendations using knowledge graphs: a probabilistic logic programming approach. In: Proceedings of the 10th ACM Conference on Recommender Systems. ACM, September 2016. https:\/\/doi.org\/10.1145\/2959100.2959131","key":"19_CR5","DOI":"10.1145\/2959100.2959131"},{"doi-asserted-by":"publisher","unstructured":"Yu, X., et al.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, February 2014. https:\/\/doi.org\/10.1145\/2556195.2556259","key":"19_CR6","DOI":"10.1145\/2556195.2556259"},{"doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Bonet, B., Koenig, S. (eds.) AAAI. pp. 2181\u20132187. AAAI Press (2015)","key":"19_CR7","DOI":"10.1609\/aaai.v29i1.9491"},{"doi-asserted-by":"publisher","unstructured":"Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, August 2016. https:\/\/doi.org\/10.1145\/2939672.2939673","key":"19_CR8","DOI":"10.1145\/2939672.2939673"},{"doi-asserted-by":"publisher","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. ACM, October 2018. https:\/\/doi.org\/10.1145\/3269206.3271739","key":"19_CR9","DOI":"10.1145\/3269206.3271739"},{"doi-asserted-by":"publisher","unstructured":"Wang, X., Wang, D., Xu, C., He, X., Cao, Y., Chua, T.S.: Explainable reasoning over knowledge graphs for recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5329\u20135336, July 2019. https:\/\/doi.org\/10.1609\/aaai.v33i01.33015329","key":"19_CR10","DOI":"10.1609\/aaai.v33i01.33015329"},{"doi-asserted-by":"publisher","unstructured":"Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference on WWW 2019. ACM Press (2019). https:\/\/doi.org\/10.1145\/3308558.3313417","key":"19_CR11","DOI":"10.1145\/3308558.3313417"},{"doi-asserted-by":"publisher","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. ACM, July 2019. https:\/\/doi.org\/10.1145\/3292500.3330836","key":"19_CR12","DOI":"10.1145\/3292500.3330836"},{"doi-asserted-by":"publisher","unstructured":"Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, July 2019. https:\/\/doi.org\/10.1145\/3331184.3331267","key":"19_CR13","DOI":"10.1145\/3331184.3331267"},{"unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (Poster). OpenReview.net (2017)","key":"19_CR14"},{"doi-asserted-by":"publisher","unstructured":"Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2008. ACM Press (2008). https:\/\/doi.org\/10.1145\/1401890.1401944","key":"19_CR15","DOI":"10.1145\/1401890.1401944"},{"doi-asserted-by":"publisher","unstructured":"Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 1\u201322 (2012). https:\/\/doi.org\/10.1145\/2168752.2168771","key":"19_CR16","DOI":"10.1145\/2168752.2168771"},{"doi-asserted-by":"publisher","unstructured":"Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, August 2013. https:\/\/doi.org\/10.1145\/2487575.2487589","key":"19_CR17","DOI":"10.1145\/2487575.2487589"},{"doi-asserted-by":"publisher","unstructured":"He, X., He, Z., Song, J., Liu, Z., Jiang, Y.G., Chua, T.S.: NAIS: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354\u20132366 (2018). https:\/\/doi.org\/10.1109\/tkde.2018.2831682","key":"19_CR18","DOI":"10.1109\/tkde.2018.2831682"},{"doi-asserted-by":"publisher","unstructured":"Zhao, H., Yao, Q., Li, J., Song, Y., Lee, D.L.: Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2017). https:\/\/doi.org\/10.1145\/3097983.3098063","key":"19_CR19","DOI":"10.1145\/3097983.3098063"},{"doi-asserted-by":"publisher","unstructured":"Hu, B., Shi, C., Zhao, W.X., Yu, P.S.: 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 & Data Mining. ACM, July 2018. https:\/\/doi.org\/10.1145\/3219819.3219965","key":"19_CR20","DOI":"10.1145\/3219819.3219965"},{"doi-asserted-by":"publisher","unstructured":"Shi, C., Hu, B., Zhao, W.X., Yu, P.S.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357\u2013370 (2019). https:\/\/doi.org\/10.1109\/tkde.2018.2833443","key":"19_CR21","DOI":"10.1109\/tkde.2018.2833443"},{"doi-asserted-by":"publisher","unstructured":"Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724\u20132743 (2017). https:\/\/doi.org\/10.1109\/tkde.2017.2754499","key":"19_CR22","DOI":"10.1109\/tkde.2017.2754499"}],"container-title":["Communications in Computer and Information Science","Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-6471-7_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:14:17Z","timestamp":1710328457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-6471-7_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811664700","9789811664717"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-6471-7_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"28 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCKS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China Conference on Knowledge Graph and Semantic Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccks2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/sigkg.cn\/ccks2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"170","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":"19","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":"9","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":"11% - 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":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","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)"}}]}}