{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T03:03:41Z","timestamp":1770519821416,"version":"3.49.0"},"reference-count":24,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2026,2,1]]},"DOI":"10.1587\/transfun.2025eap1068","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T22:07:14Z","timestamp":1755036434000},"page":"94-102","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid LSTM-SVM with Aquila Optimization Based Optimal Feature Selection for Effective Recommender System"],"prefix":"10.1587","volume":"E109.A","author":[{"given":"Nidhi","family":"BENIWAL","sequence":"first","affiliation":[{"name":"Department of Information Technology, Delhi Technological University"}]},{"given":"Om Prakash","family":"VERMA","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Delhi Technological University"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] X. Wen, \u201cUsing deep learning approach and IoT architecture to build the intelligent music recommendation system,\u201d Soft Comput., vol.25, no.4, pp.3087-3096, 2021. 10.1007\/s00500-020-05364-y","DOI":"10.1007\/s00500-020-05364-y"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] F. Jabeen, M. Maqsood, M.A. Ghazanfar, F. Aadil, S. Khan, M.F. Khan, and I. Mehmood, \u201cAn IoT based efficient hybrid recommender system for cardiovascular disease,\u201d Peer-to-Peer Netw. Appl., vol.12, pp.1263-1276, 2019. 10.1007\/s12083-019-00733-3","DOI":"10.1007\/s12083-019-00733-3"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] D. Wu, X. Luo, M. Shang, Y. He, G. Wang, and M. Zhou, \u201cA deep latent factor model for high-dimensional and sparse matrices in recommender systems,\u201d IEEE Trans. Syst., Man, Cybern., Syst., vol.51, no.7, pp.4285-4296, 2019. 10.1109\/TSMC.2019.2931393","DOI":"10.1109\/TSMC.2019.2931393"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] G.B. Martins, J.P. Papa, and H. Adeli, \u201cDeep learning techniques for recommender systems based on collaborative filtering,\u201d Expert Systems, vol.37, no.6, p.e12647, 2020. 10.1111\/exsy.12647","DOI":"10.1111\/exsy.12647"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] B. Hui, L. Zhang, X. Zhou, X. Wen, and Y. Nian, \u201cPersonalized recommendation system based on knowledge embedding and historical behavior,\u201d Appl. Intell., vol.52, pp.954-966, 2022. 10.1007\/s10489-021-02363-w","DOI":"10.1007\/s10489-021-02363-w"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] S. Zhang, Z. Jiang, J. Yao, F. Feng, K. Kuang, Z. Zhao, and F. Wu, \u201cCausal distillation for alleviating performance heterogeneity in recommender systems,\u201d IEEE Trans. Knowl. Data Eng., vol.36, no.2, pp.459-474, 2023. 10.1109\/tkde.2023.3290545","DOI":"10.1109\/TKDE.2023.3290545"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] H.W. An and N. Moon, \u201cDesign of recommendation system for tourist spot using sentiment analysis based on CNN-LSTM,\u201d J. Ambient Intell. Human. Comput., vol.13, no.3, pp.1653-1663, 2022. 10.1007\/s12652-019-01521-w","DOI":"10.1007\/s12652-019-01521-w"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] L. Wu, X. He, X. Wang, K. Zhang, and M. Wang, \u201cA survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation,\u201d IEEE Trans. Knowl. Data Eng., vol.35, no.5, pp.4425-4445, 2022. 10.1109\/tkde.2022.3145690","DOI":"10.1109\/TKDE.2022.3145690"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] B. Ray, A. Garain, and R. Sarkar, \u201cAn ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews,\u201d Applied Soft Computing, vol.98, 106935, 2021. 10.1016\/j.asoc.2020.106935","DOI":"10.1016\/j.asoc.2020.106935"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] H. Liu, C. Zheng, D. Li, X. Shen, K. Lin, J. Wang, and N.N. Xiong, \u201cEDMF: Efficient deep matrix factorization with review feature learning for industrial recommender system,\u201d IEEE Trans. Ind. Inf., vol.18, no.7, pp.4361-4371, 2021. 10.1109\/tii.2021.3128240","DOI":"10.1109\/TII.2021.3128240"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] Z. Abbasi-Moud, H. Vahdat-Nejad, and J. Sadri, \u201cTourism recommendation system based on semantic clustering and sentiment analysis,\u201d Expert Systems with Applications, vol.167, 114324, 2021. 10.1016\/j.eswa.2020.114324","DOI":"10.1016\/j.eswa.2020.114324"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] N. Beniwal and O.P. Verma, \u201cBidirectional short-term memory with osprey optimization algorithm for automatic recommendation system,\u201d Proc. International Conference on Recent Innovations in Computing, ICRIC 2023, Lecture Notes in Electrical Engineering, vol.1195, pp.319-340, 2024. 10.1007\/978-981-97-3442-9_22","DOI":"10.1007\/978-981-97-3442-9_22"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] N. Beniwal and O.P. Verma, \u201cAI-based product recommendation model using lyre bird optimization-based jordan recurrent neural network,\u201d Artificial Intelligence and Applications, ICAIA 2024, Algorithms for Intelligent Systems, A. Yadav, A.M. Joshi, M. Ergezer, and V.E. Balas, eds., pp.65-83, Springer, Singapore, 2025. 10.1007\/978-981-97-8074-7_6","DOI":"10.1007\/978-981-97-8074-7_6"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] S.M. Sekhar, G.M. Siddesh, M. Raj, and S.S. Manvi, \u201cProtein class prediction based on count vectorizer and long short term memory,\u201d Int. J. Inf. Tecnol., vol.13, no.1, pp.341-348, 2021. 10.1007\/s41870-020-00528-3","DOI":"10.1007\/s41870-020-00528-3"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] C. Ji, L. He, and W. Dai, \u201cDouble-norm constrained image denoising algorithm based on dictionary learning sparsity and FCM structure clustering,\u201d IEEE Access, vol.10, pp.128304-128317, 2022. 10.1109\/access.2022.3226501","DOI":"10.1109\/ACCESS.2022.3226501"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] T.T. Wong and P.Y. Yeh, \u201cReliable accuracy estimates from <i>k<\/i>-fold cross validation,\u201d IEEE Trans. Knowl. Data Eng., vol.32, no.8, pp.1586-1594, 2019. 10.1109\/tkde.2019.2912815","DOI":"10.1109\/TKDE.2019.2912815"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] F. Shahid, A. Zameer, and M. Muneeb, \u201cPredictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM,\u201d Chaos, Solitons &amp; Fractals, vol.140, 110212, 2020. 10.1016\/j.chaos.2020.110212","DOI":"10.1016\/j.chaos.2020.110212"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] J. Gu and S. Lu, \u201cAn effective intrusion detection approach using SVM with na\u0131\u0308ve Bayes feature embedding,\u201d Computers &amp; Security, vol.103, 102158, 2021. 10.1016\/j.cose.2020.102158","DOI":"10.1016\/j.cose.2020.102158"},{"key":"19","unstructured":"[19] Dataset 1: https:\/\/www.kaggle.com\/c\/home-depot-product-search-relevance\/overview"},{"key":"20","unstructured":"[20] Dataset 2: https:\/\/www.kaggle.com\/code\/currie32\/summarizing-text-with-amazon-reviews\/input?select=Reviews.csv"},{"key":"21","unstructured":"[21] Dataset 3: https:\/\/github.com\/sit-2021-int214\/005-Disney-Hotstar\/blob\/main\/disney_plus_shows.csv#L2"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] B. Bai, Y. Fan, W. Tan, and J. Zhang, \u201cDLTSR: A deep learning framework for recommendations of long-tail web services,\u201d IEEE Trans. Serv. Comput., vol.13, no.1, pp.73-85, 2017. 10.1109\/TSC.2017.2681666","DOI":"10.1109\/TSC.2017.2681666"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] X. Shen, B. Yi, H. Liu, W. Zhang, Z. Zhang, S. Liu, and N. Xiong, \u201cDeep variational matrix factorization with knowledge embedding for recommendation system,\u201d IEEE Trans. Knowl. Data Eng., vol.33, no.5, pp.1906-1918, 2019. 10.1109\/tkde.2019.2952849","DOI":"10.1109\/TKDE.2019.2952849"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] F. Zhuang, Z. Zhang, M. Qian, C. Shi, X. Xie, and Q. He, \u201cRepresentation learning via dual-autoencoder for recommendation,\u201d Neural Networks, vol.90, pp.83-89, 2017. 10.1016\/j.neunet.2017.03.009","DOI":"10.1016\/j.neunet.2017.03.009"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E109.A\/2\/E109.A_2025EAP1068\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T03:25:15Z","timestamp":1770434715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E109.A\/2\/E109.A_2025EAP1068\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,1]]},"references-count":24,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2025eap1068","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,1]]},"article-number":"2025EAP1068"}}