{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:39:28Z","timestamp":1777696768078,"version":"3.51.4"},"reference-count":39,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Data Analysis: An International Journal"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>In recent years, sequential recommendation has received widespread attention for its role in enhancing user experience and driving personalized content recommendations. However, it also encounters challenges, including the limitations of modeling information and the variability of user preferences. A novel time-aware Long-Short Term Transformer (TLSTSRec) for sequential recommendation is introduced in this paper to address these challenges. TLSTSRec has two major innovative features. (1)\u00a0Accurate modeling of users is achieved by fully leveraging temporal information. Time information is modeled by creating a trainable timestamp matrix from both the perspectives of time duration and time spectrum. (2) A novel time-aware Transformer model is proposed. To address the inherent variability of user preferences over time, the model combines long-term and short-term temporal information and adjusts the personalized trade-offs between long-term and short-term sequences using adaptive fusion layers. Subsequently, newly designed encoders and decoders are employed to model timestamps and interaction items. Finally, extensive experiments substantiate the effectiveness of TLSTSRec relative to various state-of-the-art sequential recommendation models based on MC\/RNN\/GNN\/SA across a spectrum of widely used metrics. Furthermore, experiments are conducted to validate the rationality of the TLSTSRec structure.<\/jats:p>","DOI":"10.3233\/ida-240051","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T09:35:18Z","timestamp":1722591318000},"page":"613-630","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["TLSTSRec: Time-aware long short-term attention neural network for sequential recommendation"],"prefix":"10.1177","volume":"29","author":[{"given":"Hongwei","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luanxuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zexi","family":"Chen","sequence":"additional","affiliation":[{"name":"Xiaomi Technology (Wuhan) Co., Ltd, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,8]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109189"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106048"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532685"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Wu B. He X. Zhang Q. Wang M. Ye Y. GCRec: Graph-augmented capsule network for next-item recommendation IEEE Transactions on Neural Networks and Learning Systems 2022.","DOI":"10.1109\/TNNLS.2022.3164982"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3426723"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.02.037"},{"key":"e_1_3_1_8_2","unstructured":"Zhang P. Kim S. A Survey on Incremental Update for Neural Recommender Systems arXiv preprint arXiv:2303.02851 2023."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_1_11_2","first-page":"4320","author":"Zhang T.","year":"2019","unstructured":"Zhang T., Zhao P., Liu Y., Sheng V.S., Xu J., Wang D., Liu G., Zhou X. et al., Feature-level Deeper Self-Attention Network for Sequential Recommendation, in: IJCAI, 2019, pp.\u00a04320\u20134326.","journal-title":"IJCAI"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498371"},{"key":"e_1_3_1_13_2","volume-title":"ECAI 2020: 24th European Conference on Artificial Intelligence 29 August\u20138 September 2020, Santiago de Compostela, Spain","author":"Xu Y.","year":"2020","unstructured":"Xu Y., Chen J., Huang C., Zhang B., Xing H., Dai P., Bo L., Joint modeling of local and global behavior dynamics for session-based recommendation, in: ECAI 2020: 24th European Conference on Artificial Intelligence 29 August\u20138 September 2020, Santiago de Compostela, Spain, IOS Press, 2020."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109894"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371786"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539475"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539253"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0030"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2881260"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3379999"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313461"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441783"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3133013"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109504"},{"key":"e_1_3_1_26_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani A.","year":"2017","unstructured":"Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser \u0141., Polosukhin I., Attention is all you need, Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_27_2","unstructured":"Guo M.-H. Xu T.-X. Liu J.-J. Liu Z.-N. Jiang P.-T. Mu T.-J. Zhang S.-H. Martin R.R. Cheng M.-M. Hu S.-M. Attention mechanisms in computer vision: A survey Computational Visual Media 2022 1\u201338."},{"key":"e_1_3_1_28_2","doi-asserted-by":"crossref","unstructured":"Lupo L. Dinarelli M. Besacier L. Divide and rule: Effective pre-training for context-aware multi-encoder translation models arXiv preprint arXiv:2103.17151 2021.","DOI":"10.18653\/v1\/2022.acl-long.312"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614773"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412247"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3356051"},{"key":"e_1_3_1_32_2","unstructured":"Shazeer N. Glu variants improve transformer arXiv preprint arXiv:2002.05202 2020."},{"key":"e_1_3_1_33_2","unstructured":"Kingma D.P. Ba J. Adam: A method for stochastic optimization arXiv preprint arXiv:1412.6980 2014."},{"key":"e_1_3_1_34_2","first-page":"4475","volume-title":"International Conference on Machine Learning","author":"Huang X.S.","year":"2020","unstructured":"Huang X.S., Perez F., Ba J., Volkovs M., Improving transformer optimization through better initialization, in: International Conference on Machine Learning, PMLR, 2020, pp.\u00a04475\u20134483."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380285"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219826"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109882"},{"key":"e_1_3_1_38_2","unstructured":"Hidasi B. Karatzoglou A. Baltrunas L. Tikk D. Session-based recommendations with recurrent neural networks arXiv preprint arXiv:1511.06939 2015."},{"key":"e_1_3_1_39_2","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N.","year":"2014","unstructured":"Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research 15 (2014), 1929\u20131958.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_1_40_2","unstructured":"Rauber P.E. Falcao A.X. Telea A.C. et al. Visualizing Time-Dependent Data Using Dynamic t-SNE 2016."}],"container-title":["Intelligent Data Analysis: An International Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDA-240051","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/IDA-240051","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IDA-240051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:20:54Z","timestamp":1777454454000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IDA-240051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.3233\/IDA-240051"],"URL":"https:\/\/doi.org\/10.3233\/ida-240051","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8]]}}}