{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T05:14:26Z","timestamp":1763702066732,"version":"3.45.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024A1515010122"],"award-info":[{"award-number":["2024A1515010122"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172283","62272315"],"award-info":[{"award-number":["62172283","62272315"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s10115-025-02569-1","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T12:07:05Z","timestamp":1756469225000},"page":"11577-11609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-stage scoring via task decoupling and fine-grained preference learning for side-information integrated sequential recommendation"],"prefix":"10.1007","volume":"67","author":[{"given":"Xiaolin","family":"Lin","sequence":"first","affiliation":[]},{"given":"Jinwei","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Mingkai","family":"He","sequence":"additional","affiliation":[]},{"given":"Weike","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"issue":"1","key":"2569_CR1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/3426723","volume":"39","author":"H Fang","year":"2020","unstructured":"Fang H, Zhang D, Shu Y, Guo G (2020) Deep learning for sequential recommendation: algorithms, influential factors, and evaluations. ACM Trans Inf Syst 39(1):10\u201311042. https:\/\/doi.org\/10.1145\/3426723","journal-title":"ACM Trans Inf Syst"},{"key":"2569_CR2","doi-asserted-by":"publisher","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on world wide web, pp 811\u2013820. https:\/\/doi.org\/10.1145\/1772690.1772773","DOI":"10.1145\/1772690.1772773"},{"key":"2569_CR3","doi-asserted-by":"publisher","unstructured":"He R, McAuley J (2016) Fusing similarity models with Markov chains for sparse sequential recommendation. In: Proceedings of the 16th IEEE international conference on data mining, pp 191\u2013200. https:\/\/doi.org\/10.1109\/ICDM.2016.0030","DOI":"10.1109\/ICDM.2016.0030"},{"key":"2569_CR4","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th international conference on learning representations. arxiv:1511.06939"},{"key":"2569_CR5","doi-asserted-by":"publisher","unstructured":"Tang J, Wang K (2018) Personalized top-N sequential recommendation via convolutional sequence embedding. In: Proceedings of the 11th ACM international conference on web search and data mining, pp 565\u2013573. https:\/\/doi.org\/10.1145\/3159652.3159656","DOI":"10.1145\/3159652.3159656"},{"key":"2569_CR6","doi-asserted-by":"publisher","unstructured":"Kang W, McAuley JJ (2018) Self-attentive sequential recommendation. In: Proceedings of the 18th IEEE international conference on data mining, pp 197\u2013206. https:\/\/doi.org\/10.1109\/ICDM.2018.00035","DOI":"10.1109\/ICDM.2018.00035"},{"key":"2569_CR7","doi-asserted-by":"publisher","unstructured":"Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: Proceedings of the 33th AAAI conference on artificial intelligence, pp 346\u2013353. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301346","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"2569_CR8","doi-asserted-by":"publisher","unstructured":"Sun F, Liu J, WuJ, Pei C, Lin X, Ou W, Jiang P (2019) 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. https:\/\/doi.org\/10.1145\/3357384.3357895","DOI":"10.1145\/3357384.3357895"},{"key":"2569_CR9","doi-asserted-by":"publisher","unstructured":"Lin J, Pan W, Ming Z (2020) FISSA: fusing item similarity models with self-attention networks for sequential recommendation. In: Proceedings of the 14th ACM conference on recommender systems, pp 130\u2013139. https:\/\/doi.org\/10.1145\/3383313.3412247","DOI":"10.1145\/3383313.3412247"},{"key":"2569_CR10","doi-asserted-by":"publisher","unstructured":"Chang J, Gao C, Zheng Y, Hui Y, Niu Y, Song Y, Jin D, Li Y (2021) 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. https:\/\/doi.org\/10.1145\/3404835.3462968","DOI":"10.1145\/3404835.3462968"},{"key":"2569_CR11","doi-asserted-by":"publisher","unstructured":"Xie X, Sun F, Liu Z, Wu S, Gao J, Zhang J, Ding B, Cui B (2022) Contrastive learning for sequential recommendation. In: 38th IEEE international conference on data engineering, pp 1259\u20131273. https:\/\/doi.org\/10.1109\/ICDE53745.2022.00099","DOI":"10.1109\/ICDE53745.2022.00099"},{"key":"2569_CR12","doi-asserted-by":"publisher","unstructured":"Qiu R, Huang Z, Yin H, Wang Z (2022) Contrastive learning for representation degeneration problem in sequential recommendation. In: Proceedings of the 15th ACM international conference on web search and data mining, pp 813\u2013823. https:\/\/doi.org\/10.1145\/3488560.3498433","DOI":"10.1145\/3488560.3498433"},{"key":"2569_CR13","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the annual conference on neural information processing systems, pp 5998\u20136008. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"issue":"10","key":"2569_CR14","doi-asserted-by":"publisher","first-page":"10112","DOI":"10.1109\/TKDE.2023.3250463","volume":"35","author":"Y Hao","year":"2023","unstructured":"Hao Y, Zhang T, Zhao P, Liu Y, Sheng VS, Xu J, Liu G, Zhou X (2023) Feature-level deeper self-attention network with contrastive learning for sequential recommendation. IEEE Trans Knowl Data Eng 35(10):10112\u201310124. https:\/\/doi.org\/10.1109\/TKDE.2023.3250463","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2569_CR15","doi-asserted-by":"publisher","unstructured":"Tanjim MM, Su C, Benjamin E, Hu D, Hong L, McAuley JJ (2020) Attentive sequential models of latent intent for next item recommendation. In: Proceedings of the 29th international conference on world wide web, pp 2528\u20132534. https:\/\/doi.org\/10.1145\/3366423.3380002","DOI":"10.1145\/3366423.3380002"},{"issue":"3","key":"2569_CR16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1145\/3441642","volume":"39","author":"Y Xu","year":"2021","unstructured":"Xu Y, Zhu Y, Yu J (2021) Modeling multiple coexisting category-level intentions for next item recommendation. ACM Trans Inf Syst 39(3):23\u201312324. https:\/\/doi.org\/10.1145\/3441642","journal-title":"ACM Trans Inf Syst"},{"key":"2569_CR17","doi-asserted-by":"publisher","unstructured":"Liu C, Li X, Cai G, Dong Z, Zhu H, Shang L (2021) Noninvasive self-attention for side information fusion in sequential recommendation. In: Proceedings of the 35th AAAI conference on artificial intelligence, pp 4249\u20134256. https:\/\/doi.org\/10.1609\/aaai.v35i5.16549","DOI":"10.1609\/aaai.v35i5.16549"},{"key":"2569_CR18","doi-asserted-by":"publisher","unstructured":"Li J, Zhao T, Li J, Chan J, Faloutsos C, Karypis G, Pantel S, McAuley JJ (2022) Coarse-to-fine sparse sequential recommendation. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 2082\u20132086. https:\/\/doi.org\/10.1145\/3477495.3531732","DOI":"10.1145\/3477495.3531732"},{"key":"2569_CR19","doi-asserted-by":"publisher","unstructured":"Xie Y, Zhou P, Kim S (2022) Decoupled side information fusion for sequential recommendation. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 1611\u20131621. https:\/\/doi.org\/10.1145\/3477495.3531963","DOI":"10.1145\/3477495.3531963"},{"key":"2569_CR20","doi-asserted-by":"publisher","unstructured":"Wang S, Shen B, Min X, He Y, Zhang X, Zhang L, Zhou J, Mo L (2024) Aligned side information fusion method for sequential recommendation. In: Companion proceedings of the 33rd international conference on world wide web, pp 112\u2013120. https:\/\/doi.org\/10.1145\/3589335.3648308","DOI":"10.1145\/3589335.3648308"},{"key":"2569_CR21","doi-asserted-by":"publisher","unstructured":"Lin X, Luo J, Pan J, Pan W, Ming Z, Liu X, Huang S, Jiang J (2024) Multi-sequence attentive user representation learning for side-information integrated sequential recommendation. In: Proceedings of the 17th ACM international conference on web search and data mining, pp 414\u2013423. https:\/\/doi.org\/10.1145\/3616855.3635815","DOI":"10.1145\/3616855.3635815"},{"key":"2569_CR22","doi-asserted-by":"publisher","unstructured":"Kabbur S, Ning X, Karypis G (2013) FISM: Factored item similarity models for top-N recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 659\u2013667. https:\/\/doi.org\/10.1145\/2487575.2487589","DOI":"10.1145\/2487575.2487589"},{"key":"2569_CR23","doi-asserted-by":"publisher","unstructured":"Yu F, Liu Q, Wu S, Wang L, Tan T (2016) A dynamic recurrent model for next basket recommendation. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, pp 729\u2013732. https:\/\/doi.org\/10.1145\/2911451.2914683","DOI":"10.1145\/2911451.2914683"},{"key":"2569_CR24","doi-asserted-by":"publisher","unstructured":"Hidasi B, Karatzoglou A (2018) Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 843\u2013852. https:\/\/doi.org\/10.1145\/3269206.3271761","DOI":"10.1145\/3269206.3271761"},{"key":"2569_CR25","doi-asserted-by":"publisher","unstructured":"Quadrana M, Karatzoglou A, Hidasi B, Cremonesi P (2017) Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the 11th ACM conference on recommender systems, pp 130\u2013137. https:\/\/doi.org\/10.1145\/3109859.3109896","DOI":"10.1145\/3109859.3109896"},{"key":"2569_CR26","doi-asserted-by":"publisher","unstructured":"Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J (2017) Neural attentive session-based recommendation. In: Proceedings of the 26th ACM international conference on information and knowledge management, pp 1419\u20131428. https:\/\/doi.org\/10.1145\/3132847.3132926","DOI":"10.1145\/3132847.3132926"},{"key":"2569_CR27","doi-asserted-by":"publisher","unstructured":"Yuan F, Karatzoglou A, Arapakis I, Jose JM, He X (2019) A simple convolutional generative network for next item recommendation. In: Proceedings of the 12th ACM international conference on web search and data mining, pp 582\u2013590. https:\/\/doi.org\/10.1145\/3289600.3290975","DOI":"10.1145\/3289600.3290975"},{"key":"2569_CR28","doi-asserted-by":"publisher","unstructured":"Ma C, Ma L, Zhang Y, Sun J, Liu X, Coates M (2020) Memory augmented graph neural networks for sequential recommendation. In: Proceedings of the 34th AAAI conference on artificial intelligence, pp 5045\u20135052. https:\/\/doi.org\/10.1609\/aaai.v34i04.5945","DOI":"10.1609\/aaai.v34i04.5945"},{"issue":"5","key":"2569_CR29","doi-asserted-by":"publisher","first-page":"2945","DOI":"10.1007\/s10115-023-02058-3","volume":"66","author":"X Han","year":"2024","unstructured":"Han X, Chen X, Zhao M, Liu T (2024) Session-based recommendation with fusion of hypergraph item global and context features. Knowl Inf Syst 66(5):2945\u20132963. https:\/\/doi.org\/10.1007\/s10115-023-02058-3","journal-title":"Knowl Inf Syst"},{"key":"2569_CR30","doi-asserted-by":"publisher","unstructured":"Zhou K, Yu H, Zhao WX, Wen J-R (2022) Filter-enhanced MLP is all you need for sequential recommendation. In: Proceedings of the 31st international conference on world wide web, pp 2388\u20132399. https:\/\/doi.org\/10.1145\/3485447.3512111","DOI":"10.1145\/3485447.3512111"},{"key":"2569_CR31","doi-asserted-by":"publisher","unstructured":"Li M, Zhang Z, Zhao X, Wang W, Zhao M, Wu R, Guo R (2023) Automlp: automated MLP for sequential recommendations. In: Proceedings of the 32nd international conference on world wide web, pp 1190\u20131198. https:\/\/doi.org\/10.1145\/3543507.3583440","DOI":"10.1145\/3543507.3583440"},{"key":"2569_CR32","doi-asserted-by":"publisher","unstructured":"Zhou P, Ye Q, Xie Y, Gao J, Wang S, Kim JB, You C, Kim S (2023) Attention calibration for transformer-based sequential recommendation. In: Proceedings of the 32nd ACM international conference on information and knowledge management, pp 3595\u20133605. https:\/\/doi.org\/10.1145\/3583780.3614785","DOI":"10.1145\/3583780.3614785"},{"issue":"3","key":"2569_CR33","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1007\/s10115-023-01996-2","volume":"66","author":"L Liu","year":"2024","unstructured":"Liu L (2024) Dynamic time-aware collaborative sequential recommendation with attention-based network. Knowl Inf Syst 66(3):1639\u20131655. https:\/\/doi.org\/10.1007\/s10115-023-01996-2","journal-title":"Knowl Inf Syst"},{"key":"2569_CR34","doi-asserted-by":"publisher","unstructured":"Chen Y, Liu Z, Li J, McAuley JJ, Xiong C (2022) Intent contrastive learning for sequential recommendation. In: Proceedings of the 31st international conference on World Wide Web, pp 2172\u20132182. https:\/\/doi.org\/10.1145\/3485447.3512090","DOI":"10.1145\/3485447.3512090"},{"key":"2569_CR35","doi-asserted-by":"publisher","unstructured":"Qin X, Yuan H, Zhao P, Fang J, Zhuang F, Liu G, Liu Y, Sheng VS (2023) Meta-optimized contrastive learning for sequential recommendation. In: Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval, pp 89\u201398. https:\/\/doi.org\/10.1145\/3539618.3591727","DOI":"10.1145\/3539618.3591727"},{"key":"2569_CR36","doi-asserted-by":"publisher","unstructured":"Li X, Sun A, Zhao M, Yu J, Zhu K, Jin D, Yu M, Yu R (2023) Multi-intention oriented contrastive learning for sequential recommendation. In: Proceedings of the 16th ACM international conference on web search and data mining, pp 411\u2013419. https:\/\/doi.org\/10.1145\/3539597.3570411","DOI":"10.1145\/3539597.3570411"},{"key":"2569_CR37","doi-asserted-by":"publisher","unstructured":"Qin X, Yuan H, Zhao P, Liu G, Zhuang F, Sheng VS (2024) Intent contrastive learning with cross subsequences for sequential recommendation. In: Proceedings of the 17th ACM international conference on web search and data mining, pp 548\u2013556. https:\/\/doi.org\/10.1145\/3616855.3635773","DOI":"10.1145\/3616855.3635773"},{"key":"2569_CR38","doi-asserted-by":"publisher","unstructured":"Cen Y, Zhang J, Zou X, Zhou C, Tang J (2020) Controllable multi-interest framework for recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining, pp 2942\u20132951. https:\/\/doi.org\/10.1145\/3394486.3403344","DOI":"10.1145\/3394486.3403344"},{"key":"2569_CR39","doi-asserted-by":"publisher","unstructured":"Tian Y, Chang J, Niu Y, Song Y, Li C (2022) 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. https:\/\/doi.org\/10.1145\/3477495.3532081","DOI":"10.1145\/3477495.3532081"},{"key":"2569_CR40","doi-asserted-by":"publisher","unstructured":"Xie Y, Gao J, Zhou P, Ye Q, Hua Y, Kim JB, Wu F, Kim S (2023) Rethinking multi-interest learning for candidate matching in recommender systems. In: Proceedings of the 17th ACM conference on recommender systems, pp 283\u2013293. https:\/\/doi.org\/10.1145\/3604915.3608766","DOI":"10.1145\/3604915.3608766"},{"key":"2569_CR41","doi-asserted-by":"publisher","unstructured":"Du Y, Wang Z, Sun Z, Ma Y, Liu H, Zhang J (2024) Disentangled multi-interest representation learning for sequential recommendation. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 677\u2013688. https:\/\/doi.org\/10.1145\/3637528.3671800","DOI":"10.1145\/3637528.3671800"},{"key":"2569_CR42","doi-asserted-by":"publisher","unstructured":"Zhang X, Xu B, Li C, Zhou Y, Li L, Lin H (2024) Side information-driven session-based recommendation: a survey. CoRR. https:\/\/doi.org\/10.48550\/arXiv.2402.17129. arXiv:2402.17129","DOI":"10.48550\/arXiv.2402.17129"},{"key":"2569_CR43","doi-asserted-by":"publisher","unstructured":"Hidasi B, Quadrana M, Karatzoglou A, Tikk D (2016) Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp 241\u2013248. https:\/\/doi.org\/10.1145\/2959100.2959167","DOI":"10.1145\/2959100.2959167"},{"key":"2569_CR44","doi-asserted-by":"publisher","unstructured":"Zhang T, Zhao P, Liu Y, Sheng VS, Xu J, Wang D, Liu G, Zhou X (2019) Feature-level deeper self-attention network for sequential recommendation. In: Proceedings of the 28th international joint conference on artificial intelligence, pp 4320\u20134326. https:\/\/doi.org\/10.24963\/ijcai.2019\/600","DOI":"10.24963\/ijcai.2019\/600"},{"key":"2569_CR45","doi-asserted-by":"publisher","unstructured":"Zhou K, Wang H, Zhao WX, Zhu Y, Wang S, Zhang F, Wang Z, Wen J (2020) S3-rec: self-supervised learning for sequential recommendation with mutual information maximization. In: Proceedings of the 29th ACM international conference on information and knowledge management, pp 1893\u20131902. https:\/\/doi.org\/10.1145\/3340531.3411954","DOI":"10.1145\/3340531.3411954"},{"key":"2569_CR46","doi-asserted-by":"publisher","unstructured":"Yuan X, Duan D, Tong L, Shi L, Zhang C (2021) ICAI-SR: item categorical attribute integrated sequential recommendation. In: Proceedings of the 4th international ACM SIGIR conference on research and development in information retrieval, pp 1687\u20131691. https:\/\/doi.org\/10.1145\/3404835.3463060","DOI":"10.1145\/3404835.3463060"},{"key":"2569_CR47","doi-asserted-by":"publisher","unstructured":"Liu H, Deng Z, Wang L, Peng J, Feng S (2023) Distribution-based learnable filters with side information for sequential recommendation. In: Proceedings of the 17th ACM conference on recommender systems, pp 78\u201388. https:\/\/doi.org\/10.1145\/3604915.3608782","DOI":"10.1145\/3604915.3608782"},{"issue":"6","key":"2569_CR48","doi-asserted-by":"publisher","first-page":"3231","DOI":"10.1007\/s10115-023-02057-4","volume":"66","author":"L Qiu","year":"2024","unstructured":"Qiu L, Dou M, Jing C, Liu Y (2024) Position-category-aware attention network for next-item recommendation. Knowl Inf Syst 66(6):3231\u20133259. https:\/\/doi.org\/10.1007\/s10115-023-02057-4","journal-title":"Knowl Inf Syst"},{"key":"2569_CR49","doi-asserted-by":"publisher","unstructured":"Zhang X, Xu B, Wu Y, Zhong Y, Lin H, Ma F (2024) Finerec: exploring fine-grained sequential recommendation. In: Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pp 1599\u20131608. https:\/\/doi.org\/10.1145\/3626772.3657761","DOI":"10.1145\/3626772.3657761"},{"key":"2569_CR50","doi-asserted-by":"publisher","unstructured":"Cai R, Wu J, San A, Wang C, Wang H (2021) Category-aware collaborative sequential recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 388\u2013397. https:\/\/doi.org\/10.1145\/3404835.3462832","DOI":"10.1145\/3404835.3462832"},{"key":"2569_CR51","unstructured":"Ke G, He D, Liu T (2021) Rethinking positional encoding in language pre-training. In: Proceedings of the 9th international conference on learning representations. https:\/\/openreview.net\/forum?id=09-528y2Fgf"},{"key":"2569_CR52","doi-asserted-by":"publisher","unstructured":"Krichene W, Rendle S (2020) On sampled metrics for item recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1748\u20131757. https:\/\/doi.org\/10.1145\/3394486.3403226","DOI":"10.1145\/3394486.3403226"},{"key":"2569_CR53","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th conference on uncertainty in artificial intelligence, pp 452\u2013461. https:\/\/www.auai.org\/uai2009\/papers\/UAI2009_0139_48141db02b9f0b02bc7158819ebfa2c7.pdf"},{"key":"2569_CR54","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representations. arxiv:1412.6980"},{"key":"2569_CR55","doi-asserted-by":"publisher","unstructured":"Li Y, Chen T, Zhang P, Yin H (2021) Lightweight self-attentive sequential recommendation. In: Demartini G, Zuccon G, Culpepper JS, Huang Z, Tong H (eds) Proceedings of the 30th ACM international conference on information and knowledge management, pp 967\u2013977. https:\/\/doi.org\/10.1145\/3459637.3482448","DOI":"10.1145\/3459637.3482448"},{"key":"2569_CR56","doi-asserted-by":"publisher","unstructured":"Petrov AV, Macdonald C (2022) A systematic review and replicability study of bert4rec for sequential recommendation. In: Golbeck J, Harper FM, Murdock V, Ekstrand MD, Shapira B, Basilico J, Lundgaard KT, Oldridge E (eds) Proceedings of the 16th ACM conference on recommender systems, pp 436\u2013447. https:\/\/doi.org\/10.1145\/3523227.3548487","DOI":"10.1145\/3523227.3548487"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02569-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02569-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02569-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T05:07:41Z","timestamp":1763701661000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02569-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":56,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2569"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02569-1","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"type":"print","value":"0219-1377"},{"type":"electronic","value":"0219-3116"}],"subject":[],"published":{"date-parts":[[2025,8,29]]},"assertion":[{"value":"9 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}