{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:25:29Z","timestamp":1742912729334,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434266"},{"type":"electronic","value":"9783031434273"}],"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\/978-3-031-43427-3_36","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:01:41Z","timestamp":1694898101000},"page":"602-618","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Future Augmentation with\u00a0Self-distillation in\u00a0Recommendation"],"prefix":"10.1007","author":[{"given":"Chong","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruobing","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pinzheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongqin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juntao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leyu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Beutel, A., et al.: Latent cross: making use of context in recurrent recommender systems. In: Proceedings of WSDM (2018)","DOI":"10.1145\/3159652.3159727"},{"key":"36_CR2","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of ICML (2020)"},{"key":"36_CR3","unstructured":"Ding, J., Quan, Y., Yao, Q., Li, Y., Jin, D.: Simplify and robustify negative sampling for implicit collaborative filtering (2020)"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Fan, Z., et al.: Sequential recommendation via stochastic self-attention. In: Proceedings of WWW (2022)","DOI":"10.1145\/3485447.3512077"},{"key":"36_CR5","unstructured":"Furlanello, T., Lipton, Z., Tschannen, M., Itti, L., Anandkumar, A.: Born again neural networks. In: Proceedings of ICML (2018)"},{"key":"36_CR6","doi-asserted-by":"crossref","unstructured":"Geng, S., Liu, S., Fu, Z., Ge, Y., Zhang, Y.: Recommendation as language processing (rlp): a unified pretrain, personalized prompt & predict paradigm (p5). In: Proceedings of RecSys (2022)","DOI":"10.1145\/3523227.3546767"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of CIKM (2018)","DOI":"10.1145\/3269206.3271761"},{"key":"36_CR8","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of ICLR (2016)"},{"key":"36_CR9","unstructured":"Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Hou, Y., Mu, S., Zhao, W.X., Li, Y., Ding, B., Wen, J.R.: Towards universal sequence representation learning for recommender systems. In: Proceedings of KDD (2022)","DOI":"10.1145\/3534678.3539381"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Hou, Y., et al.: Large language models are zero-shot rankers for recommender systems. arXiv preprint arXiv:2305.08845 (2023)","DOI":"10.1007\/978-3-031-56060-6_24"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Adversarial learning data augmentation for graph contrastive learning in recommendation. In: Proceedings of DASFAA (2023)","DOI":"10.1007\/978-3-031-30672-3_25"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Huang, Z., Lin, Z., Gong, Z., Chen, Y., Tang, Y.: A two-phase knowledge distillation model for graph convolutional network-based recommendation. Inter. J. Intell. Syst. (2022)","DOI":"10.1002\/int.22819"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: Proceedings of ICDM (2018)","DOI":"10.1109\/ICDM.2018.00035"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Kim, K., Ji, B., Yoon, D., Hwang, S.: Self-knowledge distillation with progressive refinement of targets. In: Proceedings of ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00650"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: Ufnrec: utilizing false negative samples for sequential recommendation. In: Proceedings of SDM (2023)","DOI":"10.1137\/1.9781611977653.ch6"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Lu, Y., et al.: Future-aware diverse trends framework for recommendation. In: Proceedings of WWW (2021)","DOI":"10.1145\/3442381.3449791"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Ma, C., Kang, P., Liu, X.: Hierarchical gating networks for sequential recommendation. In: Proceedings of KDD (2019)","DOI":"10.1145\/3292500.3330984"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Ma, C., Ma, L., Zhang, Y., Sun, J., Liu, X., Coates, M.: Memory augmented graph neural networks for sequential recommendation. In: Proceedings of AAAI (2020)","DOI":"10.1609\/aaai.v34i04.5945"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhou, C., Yang, H., Cui, P., Wang, X., Zhu, W.: Disentangled self-supervision in sequential recommenders. In: Proceedings of KDD (2020)","DOI":"10.1145\/3394486.3403091"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of SIGIR (2015)","DOI":"10.1145\/2766462.2767755"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Nie, P., et al.: Mic: model-agnostic integrated cross-channel recommender. In: Proceedings of CIKM (2022)","DOI":"10.1145\/3511808.3557081"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of RecSys (2017)","DOI":"10.1145\/3109859.3109896"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Sun, F., et al.: Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of CIKM (2019)","DOI":"10.1145\/3357384.3357895"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of WSDM (2018)","DOI":"10.1145\/3159652.3159656"},{"key":"36_CR26","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Proceedings of NeurIPS (2017)"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Vu, D.Q., Le, N., Wang, J.C.: Teaching yourself: a self-knowledge distillation approach to action recognition. IEEE Access (2021)","DOI":"10.1109\/ACCESS.2021.3099856"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Wang, R., et al.: Dcn v2: improved deep & cross network and practical lessons for web-scale learning to rank systems. In: Proceedings of WWW (2021)","DOI":"10.1145\/3442381.3450078"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Wei, Y., et al.: Contrastive learning for cold-start recommendation. In: Proceedings of MM (2021)","DOI":"10.1145\/3474085.3475665"},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Wu, J., et al.: Self-supervised graph learning for recommendation. In: Proceedings of SIGIR (2021)","DOI":"10.1145\/3404835.3462862"},{"key":"36_CR31","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Multi-view multi-behavior contrastive learning in recommendation. In: Proceedings of DASFAA (2022)","DOI":"10.1007\/978-3-031-00126-0_11"},{"key":"36_CR32","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Selective fairness in recommendation via prompts. In: Proceedings of SIGIR (2022)","DOI":"10.1145\/3477495.3531913"},{"key":"36_CR33","unstructured":"Wu, Y., et al.: Personalized prompts for sequential recommendation. arXiv preprint arXiv:2205.09666 (2022)"},{"key":"36_CR34","doi-asserted-by":"crossref","unstructured":"Xie, R., Liu, Q., Wang, L., Liu, S., Zhang, B., Lin, L.: Contrastive cross-domain recommendation in matching. In: Proceedings of KDD (2022)","DOI":"10.1145\/3534678.3539125"},{"key":"36_CR35","doi-asserted-by":"crossref","unstructured":"Xie, R., Qiu, Z., Zhang, B., Lin, L.: Multi-granularity item-based contrastive recommendation. In: Proceedings of DASFAA (2023)","DOI":"10.1007\/978-3-031-30672-3_27"},{"key":"36_CR36","doi-asserted-by":"crossref","unstructured":"Xie, R., Zhang, S., Wang, R., Xia, F., Lin, L.: A peep into the future: adversarial future encoding in recommendation. In: Proceedings of WSDM (2022)","DOI":"10.1145\/3488560.3498476"},{"key":"36_CR37","doi-asserted-by":"crossref","unstructured":"Xie, X., et al.: Contrastive learning for sequential recommendation. In: Proceedings of ICDE (2022)","DOI":"10.1109\/ICDE53745.2022.00099"},{"key":"36_CR38","doi-asserted-by":"crossref","unstructured":"Xu, C., et al.: Recurrent convolutional neural network for sequential recommendation. In: Proceedings of WWW (2019)","DOI":"10.1145\/3308558.3313408"},{"key":"36_CR39","doi-asserted-by":"crossref","unstructured":"Xu, T.B., Liu, C.L.: Data-distortion guided self-distillation for deep neural networks. In: Proceedings of AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33015565"},{"key":"36_CR40","doi-asserted-by":"crossref","unstructured":"Yuan, F., et al.: Future data helps training: modeling future contexts for session-based recommendation. In: Proceedings of WWW (2020)","DOI":"10.1145\/3366423.3380116"},{"key":"36_CR41","doi-asserted-by":"crossref","unstructured":"Yun, S., Park, J., Lee, K., Shin, J.: Regularizing class-wise predictions via self-knowledge distillation. In: Proceedings of CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01389"},{"key":"36_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Disentangling past-future modeling in sequential recommendation via dual networks. In: Proceedings of CIKM (2022)","DOI":"10.1145\/3511808.3557289"},{"key":"36_CR43","unstructured":"Zhang, J., Xie, R., Hou, Y., Zhao, W.X., Lin, L., Wen, J.R.: Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001 (2023)"},{"key":"36_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., Ma, K.: Be your own teacher: improve the performance of convolutional neural networks via self distillation. In: Proceedings of ICCV (2019)","DOI":"10.1109\/ICCV.2019.00381"},{"key":"36_CR45","doi-asserted-by":"crossref","unstructured":"Zhou, C., Ma, J., Zhang, J., Zhou, J., Yang, H.: Contrastive learning for debiased candidate generation in large-scale recommender systems. In: Proceedings of KDD (2021)","DOI":"10.1145\/3447548.3467102"},{"key":"36_CR46","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Deep interest network for click-through rate prediction. In: Proceedings of KDD (2018)","DOI":"10.1145\/3219819.3219823"},{"key":"36_CR47","doi-asserted-by":"crossref","unstructured":"Zhou, K., et al.: S3-rec: self-supervised learning for sequential recommendation with mutual information maximization. In: CIKM (2020)","DOI":"10.1145\/3340531.3411954"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43427-3_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T06:17:43Z","timestamp":1730096263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43427-3_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434266","9783031434273"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43427-3_36","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 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"<b>Ethical Statement<\/b> This work focuses on personalized recommendation. For offline evaluation, we conduct experiments on three classical public recommendation datasets. For online deployment, all sensitive information (e.g., user information) is preprocessed via data masking to protect user privacy. All user information (e.g., user historical behaviors) is collected and used in the online system with the users\u2019 consent. The trained model will only be used inside the corresponding online system.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","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":"829","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":"196","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":"0","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":"24% - 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":"3.63","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted 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)"}}]}}