{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:42:44Z","timestamp":1743050564034,"version":"3.40.3"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031282430"},{"type":"electronic","value":"9783031282447"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-28244-7_14","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T17:03:18Z","timestamp":1678986198000},"page":"216-231","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Recommendation Algorithm Based on\u00a0Deep Light Graph Convolution Network in\u00a0Knowledge Graph"],"prefix":"10.1007","author":[{"given":"Xiaobin","family":"Chen","sequence":"first","affiliation":[]},{"given":"Nanfeng","family":"Xiao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"14_CR1","unstructured":"Berg, R.V.d., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, H., He, X., Nie, L., Liu, W., Chua, T.S.: Attentive collaborative filtering: multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335\u2013344 (2017)","DOI":"10.1145\/3077136.3080797"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 27\u201334 (2020)","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7\u201310 (2016)","DOI":"10.1145\/2988450.2988454"},{"issue":"2","key":"14_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3291060","volume":"37","author":"Z Cheng","year":"2019","unstructured":"Cheng, Z., Chang, X., Zhu, L., Kanjirathinkal, R.C., Kankanhalli, M.: MMALFM: explainable recommendation by leveraging reviews and images. ACM Trans. Inf. Syst. (TOIS) 37(2), 1\u201328 (2019)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Ding, Y., Zhu, L., Kankanhalli, M.: Aspect-aware latent factor model: rating prediction with ratings and reviews. In: Proceedings of the 2018 World Wide Web Conference, pp. 639\u2013648 (2018)","DOI":"10.1145\/3178876.3186145"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Chiang, W.L., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.J.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 257\u2013266 (2019)","DOI":"10.1145\/3292500.3330925"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191\u2013198 (2016)","DOI":"10.1145\/2959100.2959190"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Fan, W., et al.: Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417\u2013426 (2019)","DOI":"10.1145\/3308558.3313488"},{"key":"14_CR10","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"14_CR12","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025\u20131035 (2017)"},{"issue":"4","key":"14_CR13","first-page":"1","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4), 1\u201319 (2015)","journal-title":"ACM Trans. Interact. Intell. Syst. (TIIS)"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, pp. 507\u2013517 (2016)","DOI":"10.1145\/2872427.2883037"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"He, X., Chen, T., Kan, M.Y., Chen, X.: Trirank: review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1661\u20131670 (2015)","DOI":"10.1145\/2806416.2806504"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: 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, pp. 639\u2013648 (2020)","DOI":"10.1145\/3397271.3401063"},{"issue":"12","key":"14_CR18","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1109\/TKDE.2018.2831682","volume":"30","author":"X He","year":"2018","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)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"14_CR19","doi-asserted-by":"crossref","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 (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 549\u2013558 (2016)","DOI":"10.1145\/2911451.2911489"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Hsieh, C.K., Yang, L., Cui, Y., Lin, T.Y., Belongie, S., Estrin, D.: Collaborative metric learning. In: Proceedings of the 26th International Conference on World Wide Web, pp. 193\u2013201 (2017)","DOI":"10.1145\/3038912.3052639"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263\u2013272. IEEE (2008)","DOI":"10.1109\/ICDM.2008.22"},{"key":"14_CR23","doi-asserted-by":"crossref","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, pp. 659\u2013667 (2013)","DOI":"10.1145\/2487575.2487589"},{"key":"14_CR24","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"14_CR25","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426\u2013434 (2008)","DOI":"10.1145\/1401890.1401944"},{"issue":"8","key":"14_CR27","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Li, M., Gan, T., Liu, M., Cheng, Z., Yin, J., Nie, L.: Long-tail hashtag recommendation for micro-videos with graph convolutional network. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 509\u2013518 (2019)","DOI":"10.1145\/3357384.3357912"},{"key":"14_CR29","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Liu, F., Cheng, Z., Zhu, L., Gao, Z., Nie, L.: Interest-aware message-passing GCN for recommendation. In: Proceedings of the Web Conference 2021, pp. 1296\u20131305 (2021)","DOI":"10.1145\/3442381.3449986"},{"key":"14_CR31","doi-asserted-by":"publisher","first-page":"34433","DOI":"10.1109\/ACCESS.2021.3061915","volume":"9","author":"D Mei","year":"2021","unstructured":"Mei, D., Huang, N., Li, X.: Light graph convolutional collaborative filtering with multi-aspect information. IEEE Access 9, 34433\u201334441 (2021)","journal-title":"IEEE Access"},{"key":"14_CR32","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)"},{"key":"14_CR33","unstructured":"Sha, X., Sun, Z., Zhang, J.: Attentive knowledge graph embedding for personalized recommendation. arXiv preprint arXiv:1910.08288 (2019)"},{"key":"14_CR34","unstructured":"Shi, C., et al.: Deep collaborative filtering with multi-aspect information in heterogeneous networks. IEEE Transactions on Knowledge and Data Engineering (2019)"},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Shi, Y., Larson, M., Hanjalic, A.: List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the fourth ACM Conference on Recommender Systems, pp. 269\u2013272 (2010)","DOI":"10.1145\/1864708.1864764"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., Tang, J.: Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 555\u2013563 (2019)","DOI":"10.1145\/3289600.3290989"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Sun, J., et al.: Multi-graph convolution collaborative filtering. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 1306\u20131311. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00165"},{"key":"14_CR38","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"14_CR39","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, pp. 968\u2013977 (2019)","DOI":"10.1145\/3292500.3330836"},{"key":"14_CR40","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, pp. 3307\u20133313 (2019)","DOI":"10.1145\/3308558.3313417"},{"key":"14_CR41","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 950\u2013958 (2019)","DOI":"10.1145\/3292500.3330989"},{"key":"14_CR42","doi-asserted-by":"crossref","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, pp. 165\u2013174 (2019)","DOI":"10.1145\/3331184.3331267"},{"key":"14_CR43","doi-asserted-by":"crossref","unstructured":"Wang, X., Jin, H., Zhang, A., He, X., Xu, T., Chua, T.S.: Disentangled graph collaborative filtering. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1001\u20131010 (2020)","DOI":"10.1145\/3397271.3401137"},{"key":"14_CR44","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, R., Shi, C., Song, G., Li, Q.: Multi-component graph convolutional collaborative filtering. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6267\u20136274 (2020)","DOI":"10.1609\/aaai.v34i04.6094"},{"key":"14_CR45","unstructured":"Wu, L., Li, J., Sun, P., Hong, R., Ge, Y., Wang, M.: Diffnet++: a neural influence and interest diffusion network for social recommendation. IEEE Trans. Knowl. Data Eng. (2020)"},{"key":"14_CR46","doi-asserted-by":"crossref","unstructured":"Wu, Q., et al.: Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In: The World Wide Web Conference, pp. 2091\u20132102 (2019)","DOI":"10.1145\/3308558.3313442"},{"key":"14_CR47","unstructured":"Wu, S., Sun, F., Zhang, W., Cui, B.: Graph neural networks in recommender systems: a survey. arXiv preprint arXiv:2011.02260 (2020)"},{"key":"14_CR48","unstructured":"Wu, S., Zhang, M., Jiang, X., Ke, X., Wang, L.: Personalizing graph neural networks with attention mechanism for session-based recommendation. arXiv preprint arXiv:1910.08887 (2019)"},{"key":"14_CR49","doi-asserted-by":"crossref","unstructured":"Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974\u2013983 (2018)","DOI":"10.1145\/3219819.3219890"},{"key":"14_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shi, X., Zhao, S., King, I.: Star-GCN: stacked and reconstructed graph convolutional networks for recommender systems. arXiv preprint arXiv:1905.13129 (2019)","DOI":"10.24963\/ijcai.2019\/592"},{"key":"14_CR51","unstructured":"Zhang, M., Chen, Y.: Inductive matrix completion based on graph neural networks. arXiv preprint arXiv:1904.12058 (2019)"},{"key":"14_CR52","doi-asserted-by":"crossref","unstructured":"Zhao, J., et al.: IntentGC: a scalable graph convolution framework fusing heterogeneous information for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2347\u20132357 (2019)","DOI":"10.1145\/3292500.3330686"},{"key":"14_CR53","doi-asserted-by":"crossref","unstructured":"Zheng, L., Lu, C.T., Jiang, F., Zhang, J., Yu, P.S.: Spectral collaborative filtering. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 311\u2013319 (2018)","DOI":"10.1145\/3240323.3240343"},{"key":"14_CR54","doi-asserted-by":"crossref","unstructured":"Zhu, H., et al.: Learning tree-based deep model for recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1079\u20131088 (2018)","DOI":"10.1145\/3219819.3219826"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28244-7_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T13:37:14Z","timestamp":1709645834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28244-7_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031282430","9783031282447"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28244-7_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"45","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2023.org\/index.html?v=1.0","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":"489","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":"77","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":"83","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":"16% - 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":"3","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)"}}]}}