{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:41:07Z","timestamp":1765438867473,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031282379"},{"type":"electronic","value":"9783031282386"}],"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\/978-3-031-28238-6_58","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T17:03:18Z","timestamp":1678986198000},"page":"664-675","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Augmenting Graph Convolutional Networks with\u00a0Textual Data for\u00a0Recommendations"],"prefix":"10.1007","author":[{"given":"Sergey","family":"Volokhin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcus D.","family":"Collins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oleg","family":"Rokhlenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eugene","family":"Agichtein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Chen, B., et al.: TGCN: tag graph convolutional network for tag-aware recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM 2020, pp. 155\u2013164. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3340531.3411927","key":"58_CR1","DOI":"10.1145\/3340531.3411927"},{"doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785\u2013794. ACM, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","key":"58_CR2","DOI":"10.1145\/2939672.2939785"},{"doi-asserted-by":"publisher","unstructured":"Demartini, G., et al.: Conditional graph attention networks for distilling and refining knowledge graphs in recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1834\u20131843 (2021). https:\/\/doi.org\/10.1145\/3459637.3482331","key":"58_CR3","DOI":"10.1145\/3459637.3482331"},{"key":"58_CR4","doi-asserted-by":"publisher","first-page":"51587","DOI":"10.1109\/access.2022.3174197","volume":"10","author":"Y Deng","year":"2022","unstructured":"Deng, Y.: Recommender systems based on graph embedding techniques: a review. IEEE Access 10, 51587\u201351633 (2022). https:\/\/doi.org\/10.1109\/access.2022.3174197","journal-title":"IEEE Access"},{"unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)","key":"58_CR5"},{"unstructured":"Frederickson, B.: Fast python collaborative filtering for implicit datasets (2017). https:\/\/github.com\/benfred\/implicit","key":"58_CR6"},{"unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf","key":"58_CR7"},{"doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1145\/2872427.2883037","key":"58_CR8","DOI":"10.1145\/2872427.2883037"},{"doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1145\/3397271.3401063","key":"58_CR9","DOI":"10.1145\/3397271.3401063"},{"doi-asserted-by":"publisher","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016). https:\/\/doi.org\/10.48550\/arXiv.1609.02907","key":"58_CR10","DOI":"10.48550\/arXiv.1609.02907"},{"unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/5d44ee6f2c3f71b73125876103c8f6c4-Paper.pdf","key":"58_CR11"},{"key":"58_CR12","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). https:\/\/doi.org\/10.1109\/access.2021.3061915","journal-title":"IEEE Access"},{"doi-asserted-by":"publisher","unstructured":"Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. 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), pp. 188\u2013197 (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1018","key":"58_CR13","DOI":"10.18653\/v1\/D19-1018"},{"doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2019). https:\/\/arxiv.org\/abs\/1908.10084","key":"58_CR14","DOI":"10.18653\/v1\/D19-1410"},{"doi-asserted-by":"publisher","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452\u2013461. AUAI Press, Arlington (2009). https:\/\/doi.org\/10.48550\/arXiv.1205.2618","key":"58_CR15","DOI":"10.48550\/arXiv.1205.2618"},{"doi-asserted-by":"publisher","unstructured":"Sun, R., et al.: Multi-modal knowledge graphs for recommender systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1405\u20131414 (2020). https:\/\/doi.org\/10.1145\/3340531.3411947","key":"58_CR16","DOI":"10.1145\/3340531.3411947"},{"doi-asserted-by":"publisher","unstructured":"Teredesai, A., et al.: 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). https:\/\/doi.org\/10.1145\/3292500.3330989","key":"58_CR17","DOI":"10.1145\/3292500.3330989"},{"doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017). https:\/\/doi.org\/10.48550\/arXiv.1710.10903","key":"58_CR18","DOI":"10.48550\/arXiv.1710.10903"},{"doi-asserted-by":"publisher","unstructured":"Wang, S., et al.: Graph learning based recommender systems: a review. In: Zhou, Z.H. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, pp. 4644\u20134652. IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/630. 30th International Joint Conference on Artificial Intelligence, IJCAI 2021; Conference date: 19-08-2021 Through 27-08-2021","key":"58_CR19","DOI":"10.24963\/ijcai.2021\/630"},{"doi-asserted-by":"publisher","unstructured":"Wu, S., Sun, F., Zhang, W., Xie, X., Cui, B.: Graph neural networks in recommender systems: a survey. ACM Comput. Surv. (2022). https:\/\/doi.org\/10.1145\/3535101","key":"58_CR20","DOI":"10.1145\/3535101"},{"doi-asserted-by":"publisher","unstructured":"Zhang, W., Chen, T., Wang, J., Yu, Y.: Optimizing top-N collaborative filtering via dynamic negative item sampling. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 785\u2013788. Association for Computing Machinery, New York (2013). https:\/\/doi.org\/10.1145\/2484028.2484126","key":"58_CR21","DOI":"10.1145\/2484028.2484126"}],"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\/978-3-031-28238-6_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T13:52:13Z","timestamp":1709646733000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28238-6_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031282379","9783031282386"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28238-6_58","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)"}}]}}