{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:22:42Z","timestamp":1783578162295,"version":"3.55.0"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"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":["J Intell Inf Syst"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1007\/s10844-026-01023-0","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T02:47:44Z","timestamp":1768790864000},"page":"1355-1382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-step denoising retrieval with progressive mixture-of-experts for sequential recommendation"],"prefix":"10.1007","volume":"64","author":[{"given":"Yuhua","family":"Mo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglin","family":"Lian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jibin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xipeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paiyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liying","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"1023_CR1","unstructured":"Asghar, N.(2016). Yelp dataset challenge: Review rating prediction. arXiv:1605.05362"},{"key":"1023_CR2","doi-asserted-by":"publisher","unstructured":"Chang, J., Gao, C., Zheng, Y., et\u00a0al. (2021). Sequential recommendation with graph neural networks. In: Annual international ACM SIGIR conference on research and development in information retrieval (SIGIR) (pp 378\u2013387). https:\/\/doi.org\/10.1145\/3404835.3462968","DOI":"10.1145\/3404835.3462968"},{"key":"1023_CR3","doi-asserted-by":"publisher","unstructured":"Chen, Y., Liu, Z., Li, J., et\u00a0al (2022). Intent contrastive learning for sequential recommendation. In: The web conference (WWW) (pp. 2172\u20132182). https:\/\/doi.org\/10.1145\/3485447.3512090","DOI":"10.1145\/3485447.3512090"},{"key":"1023_CR4","doi-asserted-by":"publisher","unstructured":"Chen, B., Yang, Y., Cheng, Z., et al. (2025). Semantic duality in hypergraphs: Uncertainty-aware bipolar evidence aggregation for temporal knowledge graph reasoning. Expert Systems with Applications, 130162. https:\/\/doi.org\/10.1016\/j.eswa.2025.130162","DOI":"10.1016\/j.eswa.2025.130162"},{"key":"1023_CR5","doi-asserted-by":"publisher","unstructured":"Cheng, Z., Li, S., Xin, Y., et al. (2025). Precision through progression: Empowering temporal knowledge graph reasoning with knowledge-guided chain of thought. Knowledge-Based Systems, 114448. https:\/\/doi.org\/10.1016\/j.knosys.2025.114448","DOI":"10.1016\/j.knosys.2025.114448"},{"issue":"8","key":"1023_CR6","doi-asserted-by":"publisher","first-page":"4548","DOI":"10.1109\/TKDE.2025.3568289","volume":"37","author":"Z Cheng","year":"2025","unstructured":"Cheng, Z., Liu, Y., Zhong, T., et al. (2025). Disentangling inter- and intra-cascades dynamics for information diffusion prediction. IEEE Transactions on Knowledge and Data Engineering, 37(8), 4548\u20134563. https:\/\/doi.org\/10.1109\/TKDE.2025.3568289","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"1023_CR7","doi-asserted-by":"publisher","first-page":"104472","DOI":"10.1016\/j.ipm.2025.104472","volume":"63","author":"B Chen","year":"2026","unstructured":"Chen, B., Shen, H., Cheng, Z., et al. (2026). Unveiling cross-modal consistency: Taming inter-and intra-modal noise for robust multi-modal knowledge graph completion. Information Processing & Management, 63(2), 104472. https:\/\/doi.org\/10.1016\/j.ipm.2025.104472","journal-title":"Information Processing & Management"},{"key":"1023_CR8","doi-asserted-by":"publisher","unstructured":"Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for youtube recommendations. In: ACM Conference on Recommender Systems (RecSys) (pp. 191\u2013198). https:\/\/doi.org\/10.1145\/2959100.2959190","DOI":"10.1145\/2959100.2959190"},{"key":"1023_CR9","doi-asserted-by":"publisher","unstructured":"Dang, Z., Zheng, Y., Lian, X., Peng, C., Chen, Q., & Gao, X. (2025). Semi-supervised learning for anomaly traffic detection via bidirectional normalizing flows. IEEE Transactions on Network and Service Management. https:\/\/doi.org\/10.1109\/TNSM.2025.3591533","DOI":"10.1109\/TNSM.2025.3591533"},{"issue":"1","key":"1023_CR10","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10844-022-00754-0","volume":"61","author":"Y Duan","year":"2023","unstructured":"Duan, Y., Liu, P., & Lu, Y. (2023). Mhsa-gru: combining user\u2019s dynamic preferences and items\u2019 correlation to augment sequence recommendation. Journal of Intelligent Information Systems (JIIS), 61(1), 225\u2013248. https:\/\/doi.org\/10.1007\/s10844-022-00754-0","journal-title":"Journal of Intelligent Information Systems (JIIS)"},{"key":"1023_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","volume":"107","author":"S Elfwing","year":"2018","unstructured":"Elfwing, S., Uchibe, E., & Doya, K. (2018). Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107, 3\u201311. https:\/\/doi.org\/10.1016\/j.neunet.2017.12.012","journal-title":"Neural Networks"},{"issue":"1","key":"1023_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3426723","volume":"39","author":"H Fang","year":"2020","unstructured":"Fang, H., Zhang, D., Shu, Y., et al. (2020). Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Transactions on Information Systems (TOIS), 39(1), 1\u201342. https:\/\/doi.org\/10.1145\/3426723","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"issue":"2","key":"1023_CR13","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10844-018-0530-7","volume":"52","author":"A Felfernig","year":"2019","unstructured":"Felfernig, A., Polat-Erdeniz, S., Uran, C., et al. (2019). An overview of recommender systems in the internet of things. Journal of Intelligent Information Systems (JIIS), 52(2), 285\u2013309. https:\/\/doi.org\/10.1007\/s10844-018-0530-7","journal-title":"Journal of Intelligent Information Systems (JIIS)"},{"key":"1023_CR14","doi-asserted-by":"publisher","unstructured":"Hambarde, K. A., & Proenca, H. (2023). Information retrieval: recent advances and beyond. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2023.3295776","DOI":"10.1109\/ACCESS.2023.3295776"},{"key":"1023_CR15","doi-asserted-by":"publisher","unstructured":"Hao, X., Liu, Y., Xie, R., et al (2021). Adversarial feature translation for multi-domain recommendation. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (pp. 2964\u20132973). https:\/\/doi.org\/10.1145\/3447548.3467176","DOI":"10.1145\/3447548.3467176"},{"key":"1023_CR16","doi-asserted-by":"publisher","unstructured":"He, R. & McAuley, J. (2016). Fusing similarity models with markov chains for sparse sequential recommendation. In: IEEE International Conference on Data Mining (ICDM) (pp. 191\u2013200). https:\/\/doi.org\/10.1109\/ICDM.2016.0030","DOI":"10.1109\/ICDM.2016.0030"},{"key":"1023_CR17","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., et\u00a0al (2016). Session-based recommendations with recurrent neural networks. In: 4th International Conference on Learning Representations, ICLR. arXiv:1511.06939"},{"key":"1023_CR18","doi-asserted-by":"publisher","unstructured":"Hou, Y., Mu, S., Zhao, W.X., et\u00a0al (2022). Towards universal sequence representation learning for recommender systems. In: ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD) (pp. 585\u2013593). https:\/\/doi.org\/10.1145\/3534678.3539381","DOI":"10.1145\/3534678.3539381"},{"issue":"1","key":"1023_CR19","doi-asserted-by":"publisher","first-page":"104354","DOI":"10.1016\/j.ipm.2025.104354","volume":"63","author":"Z Hu","year":"2026","unstructured":"Hu, Z., Pan, Y., Li, Z., et al. (2026). Retrieval-enhanced, adaptively collaborative, and temporal-aware user behavior comprehension for llm-based sequential recommendation. Information Processing & Management, 63(1), 104354. https:\/\/doi.org\/10.1016\/j.ipm.2025.104354","journal-title":"Information Processing & Management"},{"key":"1023_CR20","doi-asserted-by":"publisher","unstructured":"Jang, S., Lee, H., Cho, H., et\u00a0al (2020). Cities: Contextual inference of tail-item embeddings for sequential recommendation. In: IEEE International Conference on Data Mining (ICDM) (pp. 202\u2013211). https:\/\/doi.org\/10.1109\/ICDM50108.2020.00029","DOI":"10.1109\/ICDM50108.2020.00029"},{"key":"1023_CR21","doi-asserted-by":"publisher","unstructured":"Kang, W.-C. & McAuley, J. (2018). Self-attentive sequential recommendation. In: IEEE International Conference on Data Mining (ICDM) (pp. 197\u2013206) https:\/\/doi.org\/10.1109\/ICDM.2018.00035","DOI":"10.1109\/ICDM.2018.00035"},{"key":"1023_CR22","unstructured":"Krizhevsky, A., Sutskever, I., et al (2012). Imagenet classification with deep convolutional neural networks. In: Conference on Neural Information Processing Systems (NeurIPS) (pp. 1106\u20131114). https:\/\/proceedings.neurips.cc\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html"},{"issue":"3","key":"1023_CR23","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s10844-022-00723-7","volume":"59","author":"J Lei","year":"2022","unstructured":"Lei, J., Li, Y., Yang, S., et al. (2022). Two-stage sequential recommendation for side information fusion and long-term and short-term preferences modeling. Journal of Intelligent Information Systems (JIIS), 59(3), 657\u2013677. https:\/\/doi.org\/10.1007\/s10844-022-00723-7","journal-title":"Journal of Intelligent Information Systems (JIIS)"},{"key":"1023_CR24","first-page":"9459","volume":"33","author":"P Lewis","year":"2020","unstructured":"Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Conference on Neural Information Processing Systems (NeurIPS), 33, 9459\u20139474.","journal-title":"Conference on Neural Information Processing Systems (NeurIPS)"},{"key":"1023_CR25","doi-asserted-by":"publisher","unstructured":"Li, L., Lian, J., Zhou, X., et\u00a0al (2024). Ada-retrieval: An adaptive multi-round retrieval paradigm for sequential recommendations. In: Proceedings of the AAAI Conference on Artificial Intelligence (vol. 38, pp. 8670\u20138678). https:\/\/doi.org\/10.1609\/aaai.v38i8.28712","DOI":"10.1609\/aaai.v38i8.28712"},{"key":"1023_CR26","doi-asserted-by":"publisher","unstructured":"Li, C., Niu, X., Luo, X., et\u00a0al (2019). A review-driven neural model for sequential recommendation. In: International Joint Conference on Artificial Intelligence (IJCAI). https:\/\/doi.org\/10.24963\/ijcai.2019\/397","DOI":"10.24963\/ijcai.2019\/397"},{"key":"1023_CR27","doi-asserted-by":"publisher","unstructured":"Li, J., Ren, P., Chen, Z., et\u00a0al (2017). Neural attentive session-based recommendation. In: International Conference on Information and Knowledge Management (CIKM) (pp. 1419\u20131428). https:\/\/doi.org\/10.1145\/3132847.3132926","DOI":"10.1145\/3132847.3132926"},{"key":"1023_CR28","doi-asserted-by":"publisher","unstructured":"Lian, X., Cao, C., Liu, Y., et\u00a0al (2025a). Facing Anomalies Head-On: Network traffic anomaly detection via uncertainty-inspired inter-sample differences. In: The web conference (WWW) (pp. 3908\u20133917). https:\/\/doi.org\/10.1145\/3696410.3714621","DOI":"10.1145\/3696410.3714621"},{"key":"1023_CR29","doi-asserted-by":"publisher","unstructured":"Lian, X., Liu, Y., Wang, S., et\u00a0al (2024). Payload level anomaly network traffic detection via semi-supervised contrastive learning. In: International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (pp. 2559\u20132566) https:\/\/doi.org\/10.1109\/TrustCom63139.2024.00357","DOI":"10.1109\/TrustCom63139.2024.00357"},{"key":"1023_CR30","doi-asserted-by":"publisher","first-page":"111215","DOI":"10.1016\/J.PATCOG.2024.111215","volume":"161","author":"X Lian","year":"2025","unstructured":"Lian, X., Zheng, Y., Dang, Z., et al. (2025). Semi-supervised anomaly traffic detection via multi-frequency reconstruction. Pattern Recognition, 161, 111215. https:\/\/doi.org\/10.1016\/J.PATCOG.2024.111215","journal-title":"Pattern Recognition"},{"issue":"3","key":"1023_CR31","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s10844-023-00795-z","volume":"61","author":"Y Li","year":"2023","unstructured":"Li, Y., & Furukawa, T. (2023). Information gain based dynamic support set construction for cold-start recommendation. Journal of Intelligent Information Systems (JIIS), 61(3), 717\u2013737. https:\/\/doi.org\/10.1007\/s10844-023-00795-z","journal-title":"Journal of Intelligent Information Systems (JIIS)"},{"key":"1023_CR32","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.inffus.2020.10.008","volume":"67","author":"Y Li","year":"2021","unstructured":"Li, Y., Ma, J., & Zhang, Y. (2021). Image retrieval from remote sensing big data: A survey. Information Fusion, 67, 94\u2013115. https:\/\/doi.org\/10.1016\/j.inffus.2020.10.008","journal-title":"Information Fusion"},{"key":"1023_CR33","doi-asserted-by":"publisher","unstructured":"Liu, L., Cai, L., Zhang, C., et\u00a0al (2023). 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). https:\/\/doi.org\/10.1145\/3539618.3591717","DOI":"10.1145\/3539618.3591717"},{"key":"1023_CR34","doi-asserted-by":"publisher","unstructured":"Liu, X., Wang, X., Li, C., et al. (2025). Mapping the unseen: Robust ip geolocation through the lens of uncertainty quantification. Computer Networks, 111405. https:\/\/doi.org\/10.1016\/j.comnet.2025.111405","DOI":"10.1016\/j.comnet.2025.111405"},{"issue":"3","key":"1023_CR35","doi-asserted-by":"publisher","first-page":"104504","DOI":"10.1016\/j.ipm.2025.104504","volume":"63","author":"X Liu","year":"2026","unstructured":"Liu, X., Tai, W., Zhong, T., et al. (2026). Tracing paths, pruning noise: Toward robust ip geolocation via topology-guided shaping and refinement. Information Processing & Management, 63(3), 104504. https:\/\/doi.org\/10.1016\/j.ipm.2025.104504","journal-title":"Information Processing & Management"},{"key":"1023_CR36","doi-asserted-by":"publisher","unstructured":"McAuley, J., Targett, C., Shi, Q., et\u00a0al (2015). Image-based recommendations on styles and substitutes. In: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 43\u201352). https:\/\/doi.org\/10.1145\/2766462.2767755","DOI":"10.1145\/2766462.2767755"},{"key":"1023_CR37","doi-asserted-by":"publisher","unstructured":"Mikolov, T., Karafi\u00e1t, M., Burget, L., et al (2010). Recurrent neural network based language model. In: Conference of the International Speech Communication Association (INTERSPEECH) (vol. 2, pp. 1045\u20131048). https:\/\/doi.org\/10.21437\/Interspeech.2010-343","DOI":"10.21437\/Interspeech.2010-343"},{"key":"1023_CR38","doi-asserted-by":"publisher","unstructured":"Mo, Y., Liu, Y., & Ye, C. (2025). othersn: Adaptive multi-round retrieval with knowledge distillation for sequential recommendation. Journal of Intelligent Information Systems (JIIS), 1\u201323. https:\/\/doi.org\/10.1007\/s10844-025-00926-8","DOI":"10.1007\/s10844-025-00926-8"},{"key":"1023_CR39","doi-asserted-by":"publisher","unstructured":"Park, Y. E., & Son, H. (2025). Ai recommendation vs. crowdsourced recommendation vs. travel expert recommendation: The moderating role of consumption goal on travel destination decision. Plos One,20(3), 0318719. https:\/\/doi.org\/10.1371\/journal.pone.0318719","DOI":"10.1371\/journal.pone.0318719"},{"key":"1023_CR40","doi-asserted-by":"publisher","unstructured":"Qin, Y., Wang, P., Li, C. (2021). The world is binary: Contrastive learning for denoising next basket recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 859\u2013868). https:\/\/doi.org\/10.1145\/3404835.3462836","DOI":"10.1145\/3404835.3462836"},{"key":"1023_CR41","doi-asserted-by":"publisher","unstructured":"Rendle, S. (2010). Factorization machines. In: IEEE International Conference on Data Mining (ICDM) (pp. 995\u20131000). https:\/\/doi.org\/10.1109\/ICDM.2010.127","DOI":"10.1109\/ICDM.2010.127"},{"key":"1023_CR42","doi-asserted-by":"publisher","unstructured":"Rendle, S., Freudenthaler, C. & Schmidt-Thieme, L. (2010). Factorizing personalized markov chains for next-basket recommendation. In: The Web Conference (WWW) (pp. 811\u2013820). https:\/\/doi.org\/10.1145\/1772690.1772773","DOI":"10.1145\/1772690.1772773"},{"key":"1023_CR43","doi-asserted-by":"crossref","unstructured":"Sherstinsky, A. (2020). Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena,404, 132304. arXiv:1808.03314","DOI":"10.1016\/j.physd.2019.132306"},{"key":"1023_CR44","doi-asserted-by":"publisher","unstructured":"Su, Y., Li, J., An, S., Xing, M., & Feng, Z. (2025). Federated weakly-supervised video anomaly detection with mixture of local-to-global experts. Information Fusion, 103256. https:\/\/doi.org\/10.1016\/j.inffus.2025.103256","DOI":"10.1016\/j.inffus.2025.103256"},{"issue":"1","key":"1023_CR45","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s10844-020-00613-w","volume":"56","author":"H Tahmasbi","year":"2021","unstructured":"Tahmasbi, H., Jalali, M., & Shakeri, H. (2021). Tscmf: Temporal and social collective matrix factorization model for recommender systems. Journal of Intelligent Information Systems (JIIS), 56(1), 169\u2013187. https:\/\/doi.org\/10.1007\/s10844-020-00613-w","journal-title":"Journal of Intelligent Information Systems (JIIS)"},{"key":"1023_CR46","doi-asserted-by":"publisher","unstructured":"Tang, J. & Wang, K. (2018). Personalized top-n sequential recommendation via convolutional sequence embedding. In: Web Search and Data Mining (WSDM) (pp. 565\u2013573). https:\/\/doi.org\/10.1145\/3159652.3159656","DOI":"10.1145\/3159652.3159656"},{"issue":"4","key":"1023_CR47","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/s00530-024-01351-7","volume":"30","author":"J Tang","year":"2024","unstructured":"Tang, J., An, S., Liu, Y., Su, Y., & Chen, J. (2024). M2ast: Mlp-mixer-based adaptive spatial-temporal graph learning for human motion prediction. Multimedia Systems, 30(4), 206. https:\/\/doi.org\/10.1007\/s00530-024-01351-7","journal-title":"Multimedia Systems"},{"key":"1023_CR48","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al (2017). Attention is all you need (vol. 30). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"1023_CR49","doi-asserted-by":"publisher","unstructured":"Wang, P., Guo, J., Lan, Y., et\u00a0al (2015). Learning hierarchical representation model for nextbasket recommendation. In: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 403\u2013412). https:\/\/doi.org\/10.1145\/2766462.2767694","DOI":"10.1145\/2766462.2767694"},{"key":"1023_CR50","doi-asserted-by":"publisher","unstructured":"Wang, S., Hu, L., Wang, Y., et\u00a0al (2019). Sequential recommender systems: Challenges, progress and prospects. In: International Joint Conference on Artificial Intelligence (IJCAI) (pp. 6332\u20136338). https:\/\/doi.org\/10.24963\/ijcai.2019\/883","DOI":"10.24963\/ijcai.2019\/883"},{"key":"1023_CR51","doi-asserted-by":"publisher","unstructured":"Wu, S., Tang, Y., Zhu, Y., et\u00a0al (2019). Session-based recommendation with graph neural networks. In: AAAI Conference on Artificial Intelligence (AAAI) (vol. 33, pp. 346\u2013353). https:\/\/doi.org\/10.1609\/aaai.v33i01.3301346","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"1023_CR52","doi-asserted-by":"publisher","unstructured":"Xie, R., Qiu, Z., Rao, J., et al (2020). Internal and contextual attention network for cold-start multi-channel matching in recommendation. In: International Joint Conference on Artificial Intelligence (IJCAI) (pp. 2732\u20132738) https:\/\/doi.org\/10.24963\/ijcai.2020\/379","DOI":"10.24963\/ijcai.2020\/379"},{"key":"1023_CR53","doi-asserted-by":"publisher","unstructured":"Yan, A., Cheng, S., Kang, W.-C., et al (2019). Cosrec: 2d convolutional neural networks for sequential recommendation. In: International Conference on Information and Knowledge Management (CIKM) (pp. 2173\u20132176). https:\/\/doi.org\/10.1145\/3357384.3358113","DOI":"10.1145\/3357384.3358113"},{"key":"1023_CR54","doi-asserted-by":"publisher","unstructured":"Yuan, F., Karatzoglou, A., Arapakis, I., et\u00a0al (2019). A simple convolutional generative network for next item recommendation. In: Web Search and Data Mining (WSDM) (pp. 582\u2013590). https:\/\/doi.org\/10.1145\/3289600.3290975","DOI":"10.1145\/3289600.3290975"},{"key":"1023_CR55","doi-asserted-by":"publisher","unstructured":"Zhang, S., Chen, L., Shen, D., Wang, C. & Xiong, H. (2025a). Hierarchical time-aware mixture of experts for multi-modal sequential recommendation. In: Proceedings of the ACM on Web Conference (WWW) (pp. 3672\u20133682). https:\/\/doi.org\/10.1145\/3696410.3714676","DOI":"10.1145\/3696410.3714676"},{"key":"1023_CR56","doi-asserted-by":"publisher","unstructured":"Zhang, C., Han, Q., Chen, R., et\u00a0al (2024). Ssdrec: Self-augmented sequence denoising for sequential recommendation. In: 2024 IEEE 40th International Conference on Data Engineering (ICDE) (pp. 803\u2013815). https:\/\/doi.org\/10.1109\/ICDE60146.2024.00067","DOI":"10.1109\/ICDE60146.2024.00067"},{"key":"1023_CR57","doi-asserted-by":"publisher","unstructured":"Zhang, S., Wang, M., Wang, W., et\u00a0al (2025). Glint-ru: Gated lightweight intelligent recurrent units for sequential recommender systems. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1 (pp. 1948\u20131959). https:\/\/doi.org\/10.1145\/3690624.3709304","DOI":"10.1145\/3690624.3709304"},{"key":"1023_CR58","doi-asserted-by":"publisher","unstructured":"Zhang, J., Xie, R., Lu, H., Sun, W., Zhao, W.X., Chen, Y., & Kang, Z. (2025b). Frequency-augmented mixture-of-heterogeneous-experts framework for sequential recommendation. In: Proceedings of the ACM on Web Conference (WWW) (pp. 2596\u20132605). https:\/\/doi.org\/10.1145\/3696410.3714663","DOI":"10.1145\/3696410.3714663"},{"key":"1023_CR59","doi-asserted-by":"publisher","unstructured":"Zhang, X., Xu, B., Wu, Y., et\u00a0al (2024). Finerec: Exploring fine-grained sequential recommendation. In: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). https:\/\/doi.org\/10.1145\/3626772.3657761","DOI":"10.1145\/3626772.3657761"},{"key":"1023_CR60","doi-asserted-by":"publisher","unstructured":"Zhong, M., Zhong, T., Wang, Y., et al. (2025). Noise resistant encrypted malicious traffic detection through kernel-enhanced contrastive view alignment. IEEE Transactions on Networking, 1\u201313. https:\/\/doi.org\/10.1109\/TON.2025.3625606","DOI":"10.1109\/TON.2025.3625606"},{"key":"1023_CR61","doi-asserted-by":"publisher","unstructured":"Zhou, P., Ye, Q., Xie, Y., et\u00a0al (2023). Attention calibration for transformer-based sequential recommendation. In: International Conference on Information and Knowledge Management (CIKM) (pp. 3595\u20133605) https:\/\/doi.org\/10.1145\/3583780.3614785","DOI":"10.1145\/3583780.3614785"},{"key":"1023_CR62","doi-asserted-by":"publisher","unstructured":"Zhou, K., Yu, H., Zhao, W.X., et\u00a0al (2022). Filter-enhanced mlp is all you need for sequential recommendation. In: The Web Conference (WWW) (pp. 2388\u20132399). https:\/\/doi.org\/10.1145\/3485447.3512111","DOI":"10.1145\/3485447.3512111"},{"key":"1023_CR63","doi-asserted-by":"publisher","unstructured":"Zhu, Z., Sefati, S., Saadatpanah, P., & Caverlee, J. (2020). Recommendation for new users and new items via randomized training and mixture-of-experts transformation. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 1121\u20131130). https:\/\/doi.org\/10.1145\/3397271.3401178","DOI":"10.1145\/3397271.3401178"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-026-01023-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-026-01023-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-026-01023-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:09:09Z","timestamp":1783577349000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-026-01023-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,19]]},"references-count":63,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["1023"],"URL":"https:\/\/doi.org\/10.1007\/s10844-026-01023-0","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,19]]},"assertion":[{"value":"13 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2026","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"}}]}}