{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T21:21:24Z","timestamp":1767993684486,"version":"3.49.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"This work was supported by National Natural Science Foundation of China major projects","award":["No.62137001"],"award-info":[{"award-number":["No.62137001"]}]},{"name":"This work was supported by National Natural Science Foundation of China major projects","award":["No.62137001"],"award-info":[{"award-number":["No.62137001"]}]},{"name":"This work was supported by National Natural Science Foundation of China major projects","award":["No.62137001"],"award-info":[{"award-number":["No.62137001"]}]},{"name":"This work was supported by National Natural Science Foundation of China major projects","award":["No.62137001"],"award-info":[{"award-number":["No.62137001"]}]},{"name":"This work was supported by National Natural Science Foundation of China major projects","award":["No.62137001"],"award-info":[{"award-number":["No.62137001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11280-025-01332-4","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T07:38:20Z","timestamp":1739173100000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive user multi-level and multi-interest preferences for sequential recommendation"],"prefix":"10.1007","volume":"28","author":[{"given":"Rongmei","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenggen","family":"Ju","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueting","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"1332_CR1","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yu, H., Zhao, W.X., Wen, J.-R.: Filter-enhanced mlp is all you need for sequential recommendation. In: Proceedings of the ACM web conference 2022, pp. 2388\u20132399 (2022)","DOI":"10.1145\/3485447.3512111"},{"key":"1332_CR2","doi-asserted-by":"crossref","unstructured":"Liu, L., Cai, L., Zhang, C., Zhao, X., Gao, J., Wang, W., Lv, Y., Fan, W., Wang, Y., He, M., et al.: Linrec: Linear attention mechanism for long-term sequential recommender systems. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 289\u2013299 (2023)","DOI":"10.1145\/3539618.3591717"},{"key":"1332_CR3","doi-asserted-by":"crossref","unstructured":"Yue, Z., Wang, Y., He, Z., Zeng, H., McAuley, J., Wang, D.: Linear recurrent units for sequential recommendation. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 930\u2013938 (2024)","DOI":"10.1145\/3616855.3635760"},{"issue":"6","key":"1332_CR4","doi-asserted-by":"publisher","first-page":"186333","DOI":"10.1007\/s11704-023-3112-y","volume":"18","author":"M Cheng","year":"2024","unstructured":"Cheng, M., Liu, Q., Zhang, W., Liu, Z., Zhao, H., Chen, E.: A general tail item representation enhancement framework for sequential recommendation. Front. Comput. Sci. 18(6), 186333 (2024)","journal-title":"Front. Comput. Sci."},{"key":"1332_CR5","doi-asserted-by":"crossref","unstructured":"Ma, C., Kang, P., Liu, X.: Hierarchical gating networks for sequential recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 825\u2013833 (2019)","DOI":"10.1145\/3292500.3330984"},{"key":"1332_CR6","doi-asserted-by":"crossref","unstructured":"Zheng, L., Fan, Z., Lu, C.-T., Zhang, J., Yu, P.S.: Gated spectral units: Modeling co-evolving patterns for sequential recommendation. In: Proceedings of the 42nd International ACM SIGIR conference on research and development in information retrieval, pp. 1077\u20131080 (2019)","DOI":"10.1145\/3331184.3331329"},{"issue":"10","key":"1332_CR7","doi-asserted-by":"publisher","first-page":"4838","DOI":"10.1109\/TKDE.2021.3049692","volume":"34","author":"B Peng","year":"2021","unstructured":"Peng, B., Ren, Z., Parthasarathy, S., Ning, X.: Ham: Hybrid associations models for sequential recommendation. IEEE Trans. Knowl. Data Eng. 34(10), 4838\u20134853 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1332_CR8","doi-asserted-by":"crossref","unstructured":"Kang, W.-C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197\u2013206 (2018)","DOI":"10.1109\/ICDM.2018.00035"},{"key":"1332_CR9","doi-asserted-by":"crossref","unstructured":"Chang, J., Gao, C., Zheng, Y., Hui, Y., Niu, Y., Song, Y., Jin, D., Li, Y.: Sequential recommendation with graph neural networks. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp. 378\u2013387 (2021)","DOI":"10.1145\/3404835.3462968"},{"key":"1332_CR10","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Tang, Y., Yan, Z., Hu, M., Du, L.: Ksrg: Knowledge-aware sequential recommendation with graph neural networks. In: 2022 26th International conference on pattern recognition (ICPR), pp. 2408\u20132414 (2022)","DOI":"10.1109\/ICPR56361.2022.9956681"},{"key":"1332_CR11","doi-asserted-by":"crossref","unstructured":"Xue, L., Yang, D., Xiao, Y.: Factorial user modeling with hierarchical graph neural network for enhanced sequential recommendation. In: 2022 IEEE international conference on multimedia and expo (ICME), pp. 01\u201306 (2022)","DOI":"10.1109\/ICME52920.2022.9859593"},{"key":"1332_CR12","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th international conference on learning representations, pp. 1\u201310 (2016)"},{"key":"1332_CR13","doi-asserted-by":"crossref","unstructured":"Xie, X., Sun, F., Liu, Z., Wu, S., Gao, J., Zhang, J., Ding, B., Cui, B.: Contrastive learning for sequential recommendation. In: 2022 IEEE 38th international conference on data engineering (ICDE), pp. 1259\u20131273 (2022)","DOI":"10.1109\/ICDE53745.2022.00099"},{"key":"1332_CR14","doi-asserted-by":"crossref","unstructured":"Xu, Z., Pan, W., Ming, Z.: A multi-view graph contrastive learning framework for cross-domain sequential recommendation. In: Proceedings of the 17th ACM conference on recommender systems, pp. 491\u2013501 (2023)","DOI":"10.1145\/3604915.3608785"},{"key":"1332_CR15","doi-asserted-by":"publisher","first-page":"123118","DOI":"10.1016\/j.eswa.2023.123118","volume":"245","author":"Y Xiao","year":"2024","unstructured":"Xiao, Y., Huang, J., Yang, J.: Tfcsrec: Time-frequency consistency based contrastive learning for sequential recommendation. Exp. Syst. Appl. 245, 123118 (2024)","journal-title":"Exp. Syst. Appl."},{"key":"1332_CR16","doi-asserted-by":"crossref","unstructured":"Chen, G., Zhang, X., Zhao, Y., Xue, C., Xiang, J.: Exploring periodicity and interactivity in multi-interest framework for sequential recommendation. In: Proceedings of the thirtieth international joint conference on artificial intelligence, pp. 1426\u20131433 (2021)","DOI":"10.24963\/ijcai.2021\/197"},{"key":"1332_CR17","doi-asserted-by":"crossref","unstructured":"Pi, Q., Bian, W., Zhou, G., Zhu, X., Gai, K.: Practice on long sequential user behavior modeling for click-through rate prediction. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 2671\u20132679 (2019)","DOI":"10.1145\/3292500.3330666"},{"key":"1332_CR18","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, Z., Wu, M., Xu, Y., Zhao, H., Huang, P., Kang, G., Chen, Q., Li, W., Lee, D.L.: Multi-interest network with dynamic routing for recommendation at tmall. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp. 2615\u20132623 (2019)","DOI":"10.1145\/3357384.3357814"},{"key":"1332_CR19","doi-asserted-by":"crossref","unstructured":"Cen, Y., Zhang, J., Zou, X., Zhou, C., Yang, H., Tang, J.: Controllable multi-interest framework for recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 2942\u20132951 (2020)","DOI":"10.1145\/3394486.3403344"},{"issue":"5","key":"1332_CR20","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1007\/s00530-024-01437-2","volume":"30","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Sun, J., An, G.: Exploring multi-dimensional interests for session-based recommendation. Multimed. Syst. 30(5), 244 (2024)","journal-title":"Multimed. Syst."},{"key":"1332_CR21","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. Advances in Neural Information Processing Systems 30 (2017)"},{"key":"1332_CR22","doi-asserted-by":"crossref","unstructured":"Xie, Y., Gao, J., Zhou, P., Ye, Q., Hua, Y., Kim, J.B., Wu, F., Kim, S.: Rethinking multi-interest learning for candidate matching in recommender systems. In: Proceedings of the 17th ACM conference on recommender systems, pp. 283\u2013293 (2023)","DOI":"10.1145\/3604915.3608766"},{"key":"1332_CR23","doi-asserted-by":"crossref","unstructured":"Chai, Z., Chen, Z., Li, C., Xiao, R., Li, H., Wu, J., Chen, J., Tang, H.: User-aware multi-interest learning for candidate matching in recommenders. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp. 1326\u20131335 (2022)","DOI":"10.1145\/3477495.3532073"},{"key":"1332_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Shen, Y.: Incremental learning for multi-interest sequential recommendation. In: 2023 IEEE 39th international conference on data engineering (ICDE), pp. 1071\u20131083 (2023)","DOI":"10.1109\/ICDE55515.2023.00087"},{"key":"1332_CR25","doi-asserted-by":"crossref","unstructured":"Tian, Y., Chang, J., Niu, Y., Song, Y., Li, C.: When multi-level meets multi-interest: A multi-grained neural model for sequential recommendation. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp. 1632\u20131641 (2022)","DOI":"10.1145\/3477495.3532081"},{"issue":"07","key":"1332_CR26","first-page":"1730","volume":"61","author":"R Zhao","year":"2024","unstructured":"Zhao, R., Sun, S., Yan, F., Peng, J., Ju, S.: Multi-interest aware sequential recommender system based on contrastive learning. J. Comput. Res. Dev. 61(07), 1730\u20131740 (2024)","journal-title":"J. Comput. Res. Dev."},{"key":"1332_CR27","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.ins.2022.01.001","volume":"589","author":"J Liao","year":"2022","unstructured":"Liao, J., Zhou, W., Luo, F., Wen, J., Gao, M., Li, X., Zeng, J.: Sociallgn: Light graph convolution network for social recommendation. Inf. Sci. 589, 595\u2013607 (2022)","journal-title":"Inf. Sci."},{"issue":"6","key":"1332_CR28","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s11280-024-01295-y","volume":"27","author":"Y Ma","year":"2024","unstructured":"Ma, Y., Gan, M.: Sequential-hierarchical attention network: Exploring the hierarchical intention feature in poi recommendation. World Wide Web 27(6), 67 (2024)","journal-title":"World Wide Web"},{"issue":"1","key":"1332_CR29","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s11280-024-01236-9","volume":"27","author":"S Ruan","year":"2024","unstructured":"Ruan, S., Yang, C., Li, D.: Knowledge-enhanced personalized hierarchical attention network for sequential recommendation. World Wide Web 27(1), 2 (2024)","journal-title":"World Wide Web"},{"key":"1332_CR30","doi-asserted-by":"crossref","unstructured":"Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on world wide web, pp. 811\u2013820 (2010)","DOI":"10.1145\/1772690.1772773"},{"key":"1332_CR31","doi-asserted-by":"crossref","unstructured":"Pasricha, R., McAuley, J.: Translation-based factorization machines for sequential recommendation. In: Proceedings of the 12th ACM conference on recommender systems, pp. 63\u201371 (2018)","DOI":"10.1145\/3240323.3240356"},{"key":"1332_CR32","doi-asserted-by":"crossref","unstructured":"Chen, T., Yin, H., Nguyen, Q.V.H., Peng, W.-C., Li, X., Zhou, X.: Sequence-aware factorization machines for temporal predictive analytics. In: 2020 IEEE 36th international conference on data engineering (ICDE), pp. 1405\u20131416 (2020)","DOI":"10.1109\/ICDE48307.2020.00125"},{"key":"1332_CR33","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.: Fusing similarity models with markov chains for sparse sequential recommendation. In: 2016 IEEE 16th international conference on data mining (ICDM), pp. 191\u2013200 (2016)","DOI":"10.1109\/ICDM.2016.0030"},{"key":"1332_CR34","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, H., Zhang, Y., Tang, J., Cao, Y., Qin, Z., Zha, H.: Sequential recommendation with user memory networks. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp. 108\u2013116 (2018)","DOI":"10.1145\/3159652.3159668"},{"key":"1332_CR35","doi-asserted-by":"crossref","unstructured":"Huang, J., Zhao, W.X., Dou, H., Wen, J.-R., Chang, E.Y.: Improving sequential recommendation with knowledge-enhanced memory networks. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp. 505\u2013514 (2018)","DOI":"10.1145\/3209978.3210017"},{"key":"1332_CR36","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: Proceedings of the 28th ACM international conference on information and knowledge management, pp. 1441\u20131450 (2019)","DOI":"10.1145\/3357384.3357895"},{"key":"1332_CR37","doi-asserted-by":"crossref","unstructured":"Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 346\u2013353 (2019)","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"1332_CR38","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 the AAAI conference on artificial intelligence, vol. 34, pp. 5045\u20135052 (2020)","DOI":"10.1609\/aaai.v34i04.5945"},{"key":"1332_CR39","doi-asserted-by":"crossref","unstructured":"Fan, Z., Liu, Z., Zhang, J., Xiong, Y., Zheng, L., Yu, P.S.: Continuous-time sequential recommendation with temporal graph collaborative transformer. In: Proceedings of the 30th ACM International conference on information & knowledge management, pp. 433\u2013442 (2021)","DOI":"10.1145\/3459637.3482242"},{"key":"1332_CR40","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Yang, L., Jiang, W., Wei, Y., Hu, Y., Wang, H.: Deep multi-interest network for click-through rate prediction. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp. 2265\u20132268 (2020)","DOI":"10.1145\/3340531.3412092"},{"key":"1332_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, X., Zou, M., Feng, Z.: Attribute simulation for item embedding enhancement in multi-interest recommendation. In: Proceedings of the 17th ACM international conference on web search and data mining, pp. 482\u2013491 (2024)","DOI":"10.1145\/3616855.3635841"},{"key":"1332_CR42","doi-asserted-by":"crossref","unstructured":"Li, S., Yang, D., Zhang, B.: Mrif: Multi-resolution interest fusion for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp. 1765\u20131768 (2020)","DOI":"10.1145\/3397271.3401240"},{"key":"1332_CR43","doi-asserted-by":"crossref","unstructured":"Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., Zhu, X., Gai, K.: Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 5941\u20135948 (2019)","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"1332_CR44","doi-asserted-by":"crossref","unstructured":"Feng, Y., Lv, F., Shen, W., Wang, M., Sun, F., Zhu, Y., Yang, K.: Deep session interest network for click-through rate prediction. arXiv:1905.06482 (2019)","DOI":"10.24963\/ijcai.2019\/319"},{"key":"1332_CR45","doi-asserted-by":"crossref","unstructured":"Dang, Y., Yang, E., Guo, G., Jiang, L., Wang, X., Xu, X., Sun, Q., Liu, H.: Uniform sequence better: Time interval aware data augmentation for sequential recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol. 37, pp. 4225\u20134232 (2023)","DOI":"10.1609\/aaai.v37i4.25540"},{"key":"1332_CR46","doi-asserted-by":"crossref","unstructured":"Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp. 565\u2013573 (2018)","DOI":"10.1145\/3159652.3159656"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01332-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-025-01332-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01332-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T15:02:51Z","timestamp":1740236571000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-025-01332-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1332"],"URL":"https:\/\/doi.org\/10.1007\/s11280-025-01332-4","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"18 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"20"}}