{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:21:52Z","timestamp":1742995312917,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031252006"},{"type":"electronic","value":"9783031252013"}],"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\/978-3-031-25201-3_6","type":"book-chapter","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T06:02:04Z","timestamp":1676008924000},"page":"72-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-guided Contrastive Learning for\u00a0Sequential Recommendation"],"prefix":"10.1007","author":[{"given":"Hui","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanwen","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjing","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor S.","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengpeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,10]]},"reference":[{"key":"6_CR1","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: ICML. Proceedings of Machine Learning Research, vol. 119, pp. 1597\u20131607. PMLR (2020)"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: Probing simile knowledge from pre-trained language models. In: ACL (2022)","DOI":"10.18653\/v1\/2022.acl-long.404"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP, pp. 1724\u20131734. ACL (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"6_CR4","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Donkers, T., Loepp, B., Ziegler, J.: Sequential user-based recurrent neural network recommendations. In: RecSys, pp. 152\u2013160. ACM (2017)","DOI":"10.1145\/3109859.3109877"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: Simcse: simple contrastive learning of sentence embeddings. In: EMNLP. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"6_CR7","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks. CoRR abs\/1406.2661 (2014)"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.B.: Momentum contrast for unsupervised visual representation learning. In: CVPR, pp. 9726\u20139735. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"6_CR9","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: ICLR (Poster) (2016)"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., Makedon, F.: A survey on contrastive self-supervised learning. CoRR abs\/2011.00362 (2020)","DOI":"10.3390\/technologies9010002"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Kang, W., McAuley, J.J.: Self-attentive sequential recommendation. In: ICDM, pp. 197\u2013206. IEEE Computer Society (2018)","DOI":"10.1109\/ICDM.2018.00035"},{"key":"6_CR12","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, B., Zang, Y., Li, A., Yin, H.: A knowledge-aware recommender with attention-enhanced dynamic convolutional network. In: CIKM, pp. 1079\u20131088 (2021)","DOI":"10.1145\/3459637.3482406"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"McAuley, J.J., Targett, C., Shi, Q., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: SIGIR, pp. 43\u201352. ACM (2015)","DOI":"10.1145\/2766462.2767755"},{"key":"6_CR15","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. CoRR abs\/1205.2618 (2012)"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW. ACM (2010)","DOI":"10.1145\/1772690.1772773"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: CIKM, pp. 1441\u20131450. ACM (2019)","DOI":"10.1145\/3357384.3357895"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: WSDM, pp. 565\u2013573. ACM (2018)","DOI":"10.1145\/3159652.3159656"},{"key":"6_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998\u20136008 (2017)"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Knowledge enhanced sports game summarization. In: WSDM (2022)","DOI":"10.1145\/3488560.3498405"},{"key":"6_CR21","unstructured":"Wang, J., et al.: A survey on cross-lingual summarization. arXiv abs\/2203.12515 (2022)"},{"key":"6_CR22","unstructured":"Xie, X., et al.: Contrastive learning for sequential recommendation. arXiv preprint arXiv:2010.14395 (2020)"},{"key":"6_CR23","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1016\/j.neucom.2020.10.066","volume":"423","author":"C Xu","year":"2021","unstructured":"Xu, C., et al.: Long- and short-term self-attention network for sequential recommendation. Neurocomputing 423, 580\u2013589 (2021)","journal-title":"Neurocomputing"},{"key":"6_CR24","unstructured":"You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. In: NeurIPS (2020)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., He, X.: A simple convolutional generative network for next item recommendation. In: WSDM. ACM (2019)","DOI":"10.1145\/3289600.3290975"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhao, P., Zhao, L., Liu, Y., Sheng, V.S., Zhou, X.: Variational self-attention network for sequential recommendation. In: ICDE. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00138"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Wang, Y., Xie, X., Chen, L., Liu, H.: Riskoracle: a minute-level citywide traffic accident forecasting framework. In: AAAI, vol. 34, pp. 1258\u20131265 (2020)","DOI":"10.1609\/aaai.v34i01.5480"},{"key":"6_CR28","unstructured":"Zhou, Z., Wang, Y., Xie, X., Chen, L., Zhu, C.: Foresee urban sparse traffic accidents: a spatiotemporal multi-granularity perspective. IEEE Trans. Knowl. Data Eng. (2020)"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: WWW, pp. 2069\u20132080. ACM\/IW3C2 (2021)","DOI":"10.1145\/3442381.3449802"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25201-3_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T06:16:30Z","timestamp":1676009790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25201-3_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031252006","9783031252013"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25201-3_6","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":"10 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2022","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":"apwebwaim2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb-waim2022.com\/proceedings","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"297","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":"75","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":"45","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":"25% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5 Demo papers + 23 workshop papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}