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Shu, J., Shen, X., Liu, H., Yi, B., & Zhang, Z. (2018). A content-based recommendation algorithm for learning resources. Multimedia Systems, 24(2), 163-173.ShuJ.ShenX.LiuH.YiB.ZhangZ. (2018). A content-based recommendation algorithm for learning resources. Multimedia Systems, 24(2), 163-173.Search in Google Scholar
Xiaojun, L. (2017). An improved clustering-based collaborative filtering recommendation algorithm. Cluster computing, 20, 1281-1288.XiaojunL. (2017). An improved clustering-based collaborative filtering recommendation algorithm. Cluster computing, 20, 1281-1288.Search in Google Scholar
Zhang, Z., Xu, G., Zhang, P., & Wang, Y. (2017). Personalized recommendation algorithm for social networks based on comprehensive trust. Applied Intelligence, 47(3), 659-669.ZhangZ.XuG.ZhangP.WangY. (2017). Personalized recommendation algorithm for social networks based on comprehensive trust. Applied Intelligence, 47(3), 659-669.Search in Google Scholar
Guo, Y., Wang, M., & Li, X. (2017). An interactive personalized recommendation system using the hybrid algorithm model. Symmetry, 9(10), 216.GuoY.WangM.LiX. (2017). An interactive personalized recommendation system using the hybrid algorithm model. Symmetry, 9(10), 216.Search in Google Scholar
Li, C., & Zhang, Y. (2020). A personalized recommendation algorithm based on large-scale real micro-blog data. Neural Computing and Applications, 32(15), 11245-11252.LiC.ZhangY. (2020). A personalized recommendation algorithm based on large-scale real micro-blog data. Neural Computing and Applications, 32(15), 11245-11252.Search in Google Scholar
Chun-mei, L., Wei, P., Yan, Q., Jie-teng, J., & Shuo, D. (2021). Personalized Recommendation Algorithm for books and its implementation. In Journal of Physics: Conference Series (Vol. 1738, No. 1, p. 012053). IOP Publishing.Chun-meiL.WeiP.YanQ.Jie-tengJ.ShuoD. (2021). Personalized Recommendation Algorithm for books and its implementation. In Journal of Physics: Conference Series (Vol. 1738, No. 1, p. 012053). IOP Publishing.Search in Google Scholar
Yang, C., Chen, X., Song, T., Jiang, B., & Liu, Q. (2018, August). A hybrid recommendation algorithm based on heuristic similarity and trust measure. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 1413-1418). IEEE.YangC.ChenX.SongT.JiangB.LiuQ. (2018, August). A hybrid recommendation algorithm based on heuristic similarity and trust measure. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 1413-1418). IEEE.Search in Google Scholar
Monga, V., Li, Y., & Eldar, Y. C. (2021). Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing. IEEE Signal Processing Magazine, 38(2), 18-44.MongaV.LiY.EldarY. C. (2021). Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing. IEEE Signal Processing Magazine, 38(2), 18-44.Search in Google Scholar
Widiastuti, N. I. (2018, August). Deep learning–now and next in text mining and natural language processing. In IOP Conference Series: Materials Science and Engineering (Vol. 407, No. 1, p. 012114). IOP Publishing.WidiastutiN. I. (2018, August). Deep learning–now and next in text mining and natural language processing. In IOP Conference Series: Materials Science and Engineering (Vol. 407, No. 1, p. 012114). IOP Publishing.Search in Google Scholar
Liang, H., Sun, X., Sun, Y., & Gao, Y. (2017). Text feature extraction based on deep learning: a review. EURASIP journal on wireless communications and networking, 2017, 1-12.LiangH.SunX.SunY.GaoY. (2017). Text feature extraction based on deep learning: a review. EURASIP journal on wireless communications and networking, 2017, 1-12.Search in Google Scholar
Wang, S., Cai, J., Lin, Q., & Guo, W. (2019). An overview of unsupervised deep feature representation for text categorization. IEEE Transactions on Computational Social Systems, 6(3), 504-517.WangS.CaiJ.LinQ.GuoW. (2019). An overview of unsupervised deep feature representation for text categorization. IEEE Transactions on Computational Social Systems, 6(3), 504-517.Search in Google Scholar
Zhong, G., Ling, X., & Wang, L. N. (2019). From shallow feature learning to deep learning: Benefits from the width and depth of deep architectures. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(1), e1255.ZhongG.LingX.WangL. N. (2019). From shallow feature learning to deep learning: Benefits from the width and depth of deep architectures. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(1), e1255.Search in Google Scholar
Sharma, S., Rana, V., & Kumar, V. (2021). Deep learning based semantic personalized recommendation system. International Journal of Information Management Data Insights, 1(2), 100028.SharmaS.RanaV.KumarV. (2021). Deep learning based semantic personalized recommendation system. International Journal of Information Management Data Insights, 1(2), 100028.Search in Google Scholar
Endo, T. (2023). Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models. Journal of Robotics Spectrum, 1, 001-012.EndoT. (2023). Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models. Journal of Robotics Spectrum, 1, 001-012.Search in Google Scholar
Jing, L., & Tian, Y. (2020). Self-supervised visual feature learning with deep neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(11), 4037-4058.JingL.TianY. (2020). Self-supervised visual feature learning with deep neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(11), 4037-4058.Search in Google Scholar
Sun, M., Konstantelos, I., & Strbac, G. (2018). A deep learning-based feature extraction framework for system security assessment. IEEE transactions on smart grid, 10(5), 5007-5020.SunM.KonstantelosI.StrbacG. (2018). A deep learning-based feature extraction framework for system security assessment. IEEE transactions on smart grid, 10(5), 5007-5020.Search in Google Scholar
Çayir, A., Yenidoğan, I., & Dağ, H. (2018, September). Feature extraction based on deep learning for some traditional machine learning methods. In 2018 3rd International conference on computer science and engineering (UBMK) (pp. 494-497). IEEE.ÇayirA.YenidoğanI.DağH. (2018, September). Feature extraction based on deep learning for some traditional machine learning methods. In 2018 3rd International conference on computer science and engineering (UBMK) (pp. 494-497). IEEE.Search in Google Scholar
Ishaque, M., & Hudec, L. (2019, May). Feature extraction using deep learning for intrusion detection system. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-5). IEEE.IshaqueM.HudecL. (2019, May). Feature extraction using deep learning for intrusion detection system. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-5). IEEE.Search in Google Scholar
Deng, L., & Zhao, Y. (2023). Deep learning-based semantic feature extraction: A literature review and future directions. ZTE communications, 21(2), 11.DengL.ZhaoY. (2023). Deep learning-based semantic feature extraction: A literature review and future directions. ZTE communications, 21(2), 11.Search in Google Scholar
Da’u, A., & Salim, N. (2020). Recommendation system based on deep learning methods: a systematic review and new directions. Artificial Intelligence Review, 53(4), 2709-2748.Da’uA.SalimN. (2020). Recommendation system based on deep learning methods: a systematic review and new directions. Artificial Intelligence Review, 53(4), 2709-2748.Search in Google Scholar
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), 1-38.ZhangS.YaoL.SunA.TayY. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), 1-38.Search in Google Scholar
Karatzoglou, A., & Hidasi, B. (2017, August). Deep learning for recommender systems. In Proceedings of the eleventh ACM conference on recommender systems (pp. 396-397).KaratzoglouA.HidasiB. (2017, August). Deep learning for recommender systems. In Proceedings of the eleventh ACM conference on recommender systems (pp. 396-397).Search in Google Scholar
Shambour, Q. (2021). A deep learning based algorithm for multi-criteria recommender systems. Knowledge-based systems, 211, 106545.ShambourQ. (2021). A deep learning based algorithm for multi-criteria recommender systems. Knowledge-based systems, 211, 106545.Search in Google Scholar
Anil, D., Vembar, A., Hiriyannaiah, S., Siddesh, G. M., & Srinivasa, K. G. (2018, December). Performance analysis of deep learning architectures for recommendation systems. In 2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) (pp. 129-136). IEEE.AnilD.VembarA.HiriyannaiahS.SiddeshG. M.SrinivasaK. G. (2018, December). Performance analysis of deep learning architectures for recommendation systems. In 2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) (pp. 129-136). IEEE.Search in Google Scholar
Ying Ji. (2024). Optimizing Collaborative Filtering Recommendation Algorithms for Knowledge Sharing in Libraries. Applied Mathematics and Nonlinear Sciences(1).JiYing (2024). Optimizing Collaborative Filtering Recommendation Algorithms for Knowledge Sharing in Libraries. Applied Mathematics and Nonlinear Sciences(1).Search in Google Scholar
Iliya Bouyukliev,Mariya Dzhumalieva Stoeva & Paskal Piperkov. (2024). Matrix Factorization and Some Fast Discrete Transforms. Axioms(8),495-495.BouyuklievIliyaStoevaMariya DzhumalievaPiperkovPaskal (2024). Matrix Factorization and Some Fast Discrete Transforms. Axioms(8),495-495.Search in Google Scholar
Carlos Valle,Carolina Mendez Orellana,Christian Herff & Maria Rodriguez Fernandez. (2024). Identification of perceived sentences using deep neural networks in EEG. Journal of neural engineering(5),056044-056044.ValleCarlosOrellanaCarolina MendezHerffChristianFernandezMaria Rodriguez (2024). Identification of perceived sentences using deep neural networks in EEG. Journal of neural engineering(5),056044-056044.Search in Google Scholar
S. El Rahmany Mariam,Hussein Mohamed Ensaf & H. Haggag Mohamed. (2021). Semantic Detection of Targeted Attacks Using DOC2VEC Embedding. Journal of Communications Software and Systems(4),334-341.MariamS. El RahmanyEnsafHussein MohamedMohamedH. Haggag (2021). Semantic Detection of Targeted Attacks Using DOC2VEC Embedding. Journal of Communications Software and Systems(4),334-341.Search in Google Scholar
Haoyuan Cheng & Qian Ai. (2023). A Cost Optimization Method Based on Adam Algorithm for Integrated Demand Response. Electronics(23).ChengHaoyuanAiQian (2023). A Cost Optimization Method Based on Adam Algorithm for Integrated Demand Response. Electronics(23).Search in Google Scholar