{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:31:12Z","timestamp":1780353072686,"version":"3.54.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Young Elite Scientists Sponsorship Program by China Association for Science and Technology","award":["2023QNRC001"],"award-info":[{"award-number":["2023QNRC001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62222215"],"award-info":[{"award-number":["62222215"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s11280-025-01381-9","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T07:03:09Z","timestamp":1762930989000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Curriculum-scheduled knowledge distillation from multiple pre-trained teachers for multi-domain sequential recommendation"],"prefix":"10.1007","volume":"28","author":[{"given":"Wenqi","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruobing","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wayne Xin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leyu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,12]]},"reference":[{"key":"1381_CR1","doi-asserted-by":"crossref","unstructured":"Hou, Y., Mu, S., Zhao, W.X., Li, Y., Ding, B., Wen, J.: Towards universal sequence representation learning for recommender systems. In: KDD. (2022)","DOI":"10.1145\/3534678.3539381"},{"key":"1381_CR2","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, M., Li, J., Fu, J., Shen, X., Shang, J., McAuley, J.: Text is all you need: Learning language representations for sequential recommendation. In: KDD. (2023)","DOI":"10.1145\/3580305.3599519"},{"key":"1381_CR3","doi-asserted-by":"crossref","unstructured":"Yuan, F., He, X., Karatzoglou, A., Zhang, L.: Parameter-efficient transfer from sequential behaviors for user modeling and recommendation. In: SIGIR. (2020)","DOI":"10.1145\/3397271.3401156"},{"key":"1381_CR4","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Wu, X., Gao, J., Fan, W.: U-bert: Pre-training user representations for improved recommendation. In: AAAI. (2021)","DOI":"10.1609\/aaai.v35i5.16557"},{"key":"1381_CR5","doi-asserted-by":"crossref","unstructured":"Mao, Z., Wang, H., Du, Y., Wong, K.-f.: Unitrec: A unified text-to-text transformer and joint contrastive learning framework for text-based recommendation. (2023). arXiv:2305.15756","DOI":"10.18653\/v1\/2023.acl-short.100"},{"key":"1381_CR6","doi-asserted-by":"crossref","unstructured":"Wang, J., Zeng, Z., Wang, Y., Wang, Y., Lu, X., Li, T., Yuan, J., Zhang, R., Zheng, H., Xia, S.: Missrec: Pre-training and transferring multi-modal interest-aware sequence representation for recommendation. In: Proceedings of the 31st ACM International Conference on Multimedia, MM 2023, Ottawa, ON, Canada, 29 October 2023- 3 November 2023. (2023)","DOI":"10.1145\/3581783.3611967"},{"key":"1381_CR7","doi-asserted-by":"crossref","unstructured":"Kang, W., McAuley, J.J.: Self-attentive sequential recommendation. In: ICDM. (2018)","DOI":"10.1109\/ICDM.2018.00035"},{"key":"1381_CR8","unstructured":"Sun, W., Xie, R., Bian, S., Zhao, W.X., Zhou, J.: Universal multi-modal multi-domain pre-trained recommendation. (2023). arXiv:2311.01831"},{"key":"1381_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, T., Zhang, P., Yin, H.: Lightweight self-attentive sequential recommendation. In: CIKM. (2021)","DOI":"10.1145\/3459637.3482448"},{"key":"1381_CR10","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yu, H., Zhao, W.X., Wen, J.: Filter-enhanced MLP is all you need for sequential recommendation. In: WWW. (2022)","DOI":"10.1145\/3485447.3512111"},{"key":"1381_CR11","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. (2015). arXiv:1503.02531"},{"key":"1381_CR12","doi-asserted-by":"crossref","unstructured":"Chen, G., Chen, J., Feng, F., Zhou, S., He, X.: Unbiased knowledge distillation for recommendation. In: WSDM. (2023)","DOI":"10.1145\/3539597.3570477"},{"key":"1381_CR13","unstructured":"Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Li, X., Zhu, C., Guo, H., Yu, Y., Tang, R., et al.: How can recommender systems benefit from large language models: a survey. (2023). arXiv:2306.05817"},{"key":"1381_CR14","doi-asserted-by":"crossref","unstructured":"Wei, W., Ren, X., Tang, J., Wang, Q., Su, L., Cheng, S., Wang, J., Yin, D., Huang, C.: Llmrec: Large language models with graph augmentation for recommendation. In: WSDM. (2024)","DOI":"10.1145\/3616855.3635853"},{"issue":"5","key":"1381_CR15","doi-asserted-by":"publisher","first-page":"2801","DOI":"10.1007\/s11280-023-01163-1","volume":"26","author":"K Sun","year":"2023","unstructured":"Sun, K., Qian, T., Zhong, M., Li, X.: Towards more effective encoders in pre-training for sequential recommendation. World Wide Web (WWW) 26(5), 2801\u20132832 (2023)","journal-title":"World Wide Web (WWW)"},{"key":"1381_CR16","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: The long-document transformer. (2020). arXiv:2004.05150"},{"key":"1381_CR17","doi-asserted-by":"crossref","unstructured":"Sheng, X., Zhao, L., Zhou, G., Ding, X., Dai, B., Luo, Q., Yang, S., Lv, J., Zhang, C., Deng, H., Zhu, X.: One model to serve all: Star topology adaptive recommender for multi-domain CTR prediction. In: CIKM \u201921: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021, pp. 4104\u20134113. (2021)","DOI":"10.1145\/3459637.3481941"},{"key":"1381_CR18","doi-asserted-by":"crossref","unstructured":"Hwang, J., Ju, H., Kang, S., Jang, S., Yu, H.: Multi-domain sequential recommendation via domain space learning. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024, pp. 2134\u20132144. (2024)","DOI":"10.1145\/3626772.3657685"},{"issue":"2","key":"1381_CR19","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/s11280-024-01240-z","volume":"27","author":"J Chen","year":"2024","unstructured":"Chen, J., Zhang, F., Li, H., Lu, H., Jin, X., Liu, K., Li, H., Wang, Y.: Empnet: An extract-map-predict neural network architecture for cross-domain recommendation. World Wide Web (WWW) 27(2), 12 (2024)","journal-title":"World Wide Web (WWW)"},{"key":"1381_CR20","doi-asserted-by":"crossref","unstructured":"Hao, X., Liu, Y., Xie, R., Ge, K., Tang, L., Zhang, X., Lin, L.: Adversarial feature translation for multi-domain recommendation. In: KDD \u201921: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021, pp. 2964\u20132973. (2021)","DOI":"10.1145\/3447548.3467176"},{"key":"1381_CR21","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Li, Q., Zhu, H., Yu, J., Li, J., Xu, Z., Dong, H., Zheng, B.: Adaptive domain interest network for multi-domain recommendation. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022, pp. 3212\u20133221. (2022)","DOI":"10.1145\/3511808.3557137"},{"key":"1381_CR22","doi-asserted-by":"crossref","unstructured":"Ma, M., Ren, P., Lin, Y., Chen, Z., Ma, J., Rijke, M.: $$\\pi$$-net: A parallel information-sharing network for shared-account cross-domain sequential recommendations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019, pp. 685\u2013694. (2019)","DOI":"10.1145\/3331184.3331200"},{"issue":"4","key":"1381_CR23","doi-asserted-by":"publisher","first-page":"4106","DOI":"10.1109\/TKDE.2021.3130927","volume":"35","author":"W Sun","year":"2023","unstructured":"Sun, W., Ma, M., Ren, P., Lin, Y., Chen, Z., Ren, Z., Ma, J., Rijke, M.: Parallel split-join networks for shared account cross-domain sequential recommendations. IEEE Trans. Knowl. Data Eng. 35(4), 4106\u20134123 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1381_CR24","doi-asserted-by":"crossref","unstructured":"Xie, R., Liu, Q., Wang, L., Liu, S., Zhang, B., Lin, L.: Contrastive cross-domain recommendation in matching. In: KDD \u201922: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022, pp. 4226\u20134236. (2022)","DOI":"10.1145\/3534678.3539125"},{"key":"1381_CR25","doi-asserted-by":"crossref","unstructured":"Hsieh, C., Li, C., Yeh, C., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C., Pfister, T.: Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes. In: Findings of ACL. (2023)","DOI":"10.18653\/v1\/2023.findings-acl.507"},{"key":"1381_CR26","doi-asserted-by":"crossref","unstructured":"Lee, Y., Kim, K.: Dual correction strategy for ranking distillation in top-n recommender system. In: CIKM. (2021)","DOI":"10.1145\/3459637.3482093"},{"key":"1381_CR27","doi-asserted-by":"crossref","unstructured":"Kang, S., Lee, D., Kweon, W., Yu, H.: Personalized knowledge distillation for recommender system. Knowl, Based Syst (2022)","DOI":"10.1016\/j.knosys.2021.107958"},{"key":"1381_CR28","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhang, Y., Xu, H., Qin, Z., Zha, H.: Adversarial distillation for efficient recommendation with external knowledge. ACM Trans. Inf, Syst (2019)","DOI":"10.1145\/3281659"},{"key":"1381_CR29","doi-asserted-by":"crossref","unstructured":"Tang, J., Wang, K.: Ranking distillation: Learning compact ranking models with high performance for recommender system. In: KDD. (2018)","DOI":"10.1145\/3219819.3220021"},{"key":"1381_CR30","doi-asserted-by":"crossref","unstructured":"Kweon, W., Kang, S., Yu, H.: Bidirectional distillation for top-k recommender system. In: WWW. (2021)","DOI":"10.1145\/3442381.3449878"},{"key":"1381_CR31","doi-asserted-by":"crossref","unstructured":"Wang, H., Lian, D., Ge, Y.: Binarized collaborative filtering with distilling graph convolutional network. In: IJCAI. (2019)","DOI":"10.24963\/ijcai.2019\/667"},{"key":"1381_CR32","doi-asserted-by":"crossref","unstructured":"Kang, S., Hwang, J., Kweon, W., Yu, H.: DE-RRD: A knowledge distillation framework for recommender system. In: CIKM. (2020)","DOI":"10.1145\/3340531.3412005"},{"key":"1381_CR33","doi-asserted-by":"crossref","unstructured":"Kang, S., Kweon, W., Lee, D., Lian, J., Xie, X., Yu, H.: Distillation from heterogeneous models for top-k recommendation. In: Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023, pp. 801\u2013811. (2023)","DOI":"10.1145\/3543507.3583209"},{"key":"1381_CR34","doi-asserted-by":"crossref","unstructured":"Du, H., Yuan, H., Zhao, P., Zhuang, F., Liu, G., Zhao, L., Liu, Y., Sheng, V.S.: Ensemble modeling with contrastive knowledge distillation for sequential recommendation. In: SIGIR. (2023)","DOI":"10.1145\/3539618.3591679"},{"key":"1381_CR35","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: RecSys. (2022)","DOI":"10.1145\/3523227.3546767"},{"key":"1381_CR36","doi-asserted-by":"crossref","unstructured":"Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., Zhao, W.X.: Large language models are zero-shot rankers for recommender systems. (2023). arXiv:2305.08845","DOI":"10.1007\/978-3-031-56060-6_24"},{"key":"1381_CR37","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, X., Fan, M., Huang, J., Yang, S., Zhu, W.: Curriculum meta-learning for next poi recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2692\u20132702. (2021)","DOI":"10.1145\/3447548.3467132"},{"key":"1381_CR38","unstructured":"Hacohen, G., Weinshall, D.: On the power of curriculum learning in training deep networks. In: International Conference on Machine Learning, pp. 2535\u20132544. PMLR (2019)"},{"key":"1381_CR39","doi-asserted-by":"crossref","unstructured":"Xu, B., Zhang, L., Mao, Z., Wang, Q., Xie, H., Zhang, Y.: Curriculum learning for natural language understanding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6095\u20136104. (2020)","DOI":"10.18653\/v1\/2020.acl-main.542"},{"key":"1381_CR40","doi-asserted-by":"crossref","unstructured":"Penha, G., Hauff, C.: Curriculum learning strategies for ir: An empirical study on conversation response ranking. (2019). arXiv:1912.08555","DOI":"10.1007\/978-3-030-45439-5_46"},{"key":"1381_CR41","doi-asserted-by":"crossref","unstructured":"Krichene, W., Rendle, S.: On sampled metrics for item recommendation. In: KDD. (2020)","DOI":"10.1145\/3394486.3403226"},{"key":"1381_CR42","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: ICLR. (2016)"},{"key":"1381_CR43","doi-asserted-by":"crossref","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., et al.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: CIKM. (2019)","DOI":"10.1145\/3357384.3357895"},{"key":"1381_CR44","doi-asserted-by":"crossref","unstructured":"Li, C., Zhao, M., Zhang, H., Yu, C., Cheng, L., Shu, G., Kong, B., Niu, D.: Recguru: Adversarial learning of generalized user representations for cross-domain recommendation. In: WSDM. (2022)","DOI":"10.1145\/3488560.3498388"},{"key":"1381_CR45","doi-asserted-by":"crossref","unstructured":"Rendle, S.: Factorization machines. In: ICDM. (2010)","DOI":"10.1109\/ICDM.2010.127"},{"key":"1381_CR46","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. In: IJCAI. (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"1381_CR47","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: SIGIR. (2020)","DOI":"10.1145\/3397271.3401063"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01381-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-025-01381-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01381-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T04:06:33Z","timestamp":1764821193000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-025-01381-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":47,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["1381"],"URL":"https:\/\/doi.org\/10.1007\/s11280-025-01381-9","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"15 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"71"}}