{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:54:21Z","timestamp":1780674861527,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"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":["User Model User-Adap Inter"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11257-024-09418-w","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T07:03:18Z","timestamp":1729666998000},"page":"2085-2114","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["TriDeepRec: a hybrid deep learning approach to content- and behavior-based recommendation systems"],"prefix":"10.1007","volume":"34","author":[{"given":"Amirhossein","family":"Ghadami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Tran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"9418_CR1","unstructured":"Lee, D., Hosanagar, K.: Impact of recommender systems on sales volume and diversity.(2014) (2014)"},{"issue":"4","key":"9418_CR2","doi-asserted-by":"publisher","first-page":"2709","DOI":"10.1007\/s10462-019-09744-1","volume":"53","author":"A Da\u2019u","year":"2020","unstructured":"Da\u2019u, A., Salim, N.: Recommendation system based on deep learning methods: a systematic review and new directions. Artificial Intelligence Review 53(4), 2709\u20132748 (2020)","journal-title":"Artificial Intelligence Review"},{"key":"9418_CR3","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.dss.2015.03.008","volume":"74","author":"J Lu","year":"2015","unstructured":"Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12\u201332 (2015)","journal-title":"Decis. Support Syst."},{"key":"9418_CR4","unstructured":"Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. arXiv Preprint arXiv:1301.7363 (2013)"},{"key":"9418_CR5","doi-asserted-by":"crossref","unstructured":"Singhal, A., Sinha, P., Pant, R.: Use of deep learning in modern recommendation system: A summary of recent works. arXiv Preprint arXiv:1712.07525 (2017)","DOI":"10.5120\/ijca2017916055"},{"issue":"35","key":"9418_CR6","doi-asserted-by":"publisher","first-page":"24783","DOI":"10.1007\/s00521-023-08958-3","volume":"35","author":"A Torkashvand","year":"2023","unstructured":"Torkashvand, A., Jameii, S.M., Reza, A.: Deep learning-based collaborative filtering recommender systems: a comprehensive and systematic review. Neural Comput. Appl. 35(35), 24783\u201324827 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"5","key":"9418_CR7","first-page":"1","volume":"56","author":"S Li","year":"2024","unstructured":"Li, S., Guo, H., Tang, X., Tang, R., Hou, L., Li, R., Zhang, R.: Embedding compression in recommender systems: A survey. ACM Computing Surveys 56(5), 1\u201321 (2024)","journal-title":"ACM Computing Surveys"},{"issue":"6","key":"9418_CR8","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TKDE.2005.99","volume":"17","author":"G Adomavicius","year":"2005","unstructured":"Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734\u2013749 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"9418_CR9","first-page":"5771","volume":"5","author":"SS Lakshmi","year":"2014","unstructured":"Lakshmi, S.S., Lakshmi, T.A.: Recommendation systems: issues and challenges. Int. J. Comput. Sci. Inform. Technol. 5(4), 5771\u20135772 (2014)","journal-title":"Int. J. Comput. Sci. Inform. Technol."},{"key":"9418_CR10","doi-asserted-by":"crossref","unstructured":"Singhal, A., Sinha, P., Pant, R.: Use of deep learning in modern recommendation system: A summary of recent works. arXiv\u00a0Preprint arXiv:1712.07525 (2017)","DOI":"10.5120\/ijca2017916055"},{"key":"9418_CR11","doi-asserted-by":"crossref","unstructured":"Guo, X., Liu, X., Zhu, E., Yin, J.: Deep clustering with convolutional autoencoders. In: Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II 24, pp. 373\u2013382 (2017). Springer","DOI":"10.1007\/978-3-319-70096-0_39"},{"key":"9418_CR12","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2003.05991","volume-title":"Autoencoders","author":"D Bank","year":"2023","unstructured":"Bank, D., Koenigstein, N., Giryes, R.: Autoencoders. Data mining and knowledge discovery handbook, Machine Learning for Data Science Handbook (2023). https:\/\/doi.org\/10.48550\/arXiv.2003.05991"},{"issue":"11","key":"9418_CR13","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proceedings of the IEEE"},{"key":"9418_CR14","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173\u2013182 (2017)","DOI":"10.1145\/3038912.3052569"},{"issue":"8","key":"9418_CR15","doi-asserted-by":"publisher","first-page":"4591","DOI":"10.1109\/TII.2019.2893714","volume":"15","author":"B Yi","year":"2019","unstructured":"Yi, B., Shen, X., Liu, H., Zhang, Z., Zhang, W., Liu, S., Xiong, N.: Deep matrix factorization with implicit feedback embedding for recommendation system. IEEE Trans. Ind. Inform. 15(8), 4591\u20134601 (2019)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"7","key":"9418_CR16","first-page":"579","volume":"8","author":"M-C Popescu","year":"2009","unstructured":"Popescu, M.-C., Balas, V.E., Perescu-Popescu, L., Mastorakis, N.: Multilayer perceptron and neural networks. WSEAS Trans. Circuits Syst. 8(7), 579\u2013588 (2009)","journal-title":"WSEAS Trans. Circuits Syst."},{"key":"9418_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105020","volume":"185","author":"R Yin","year":"2019","unstructured":"Yin, R., Li, K., Zhang, G., Lu, J.: A deeper graph neural network for recommender systems. Knowl.-Based Syst. 185, 105020 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"9418_CR18","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ins.2022.01.033","volume":"592","author":"S Lee","year":"2022","unstructured":"Lee, S., Kim, D.: Deep learning based recommender system using cross convolutional filters. Inform. Sci. 592, 112\u2013122 (2022)","journal-title":"Inform. Sci."},{"key":"9418_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-023-00467-9","author":"S Omidvar","year":"2023","unstructured":"Omidvar, S., Tran, T.: Tackling cold start with deep personalized transfer of user preferences for cross-domain recommendation. Int. J. Data Sci. Anal. (2023). https:\/\/doi.org\/10.1007\/s41060-023-00467-9","journal-title":"Int. J. Data Sci. Anal."},{"key":"9418_CR20","doi-asserted-by":"crossref","unstructured":"Wang, R., Shivanna, R., Cheng, D.Z., Jain, S., Lin, D., Hong, L., Chi, E.H.: Dcn-m: Improved deep & cross network for feature cross learning in web-scale learning to rank systems. arXiv\u00a0Preprint arXiv:2008.13535 (2020)","DOI":"10.1145\/3442381.3450078"},{"key":"9418_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, S.-Z., Li, P.-H., Chen, X.-N.: Collaborative convolution autoencoder for recommendation systems. In: Proceedings of the 2019 8th International Conference on Networks, Communication and Computing, pp. 202\u2013207 (2019)","DOI":"10.1145\/3375998.3376031"},{"key":"9418_CR22","doi-asserted-by":"crossref","unstructured":"Switrayana, I.N., Maulidevi, N.U.: Collaborative convolutional autoencoder for scientific article recommendation. In: 2022 9th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 96\u2013101 (2022). IEEE","DOI":"10.1109\/ICITACEE55701.2022.9924130"},{"key":"9418_CR23","first-page":"3203","volume":"17","author":"H-J Xue","year":"2017","unstructured":"Xue, H.-J., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. IJCAI 17, 3203\u20133209 (2017)","journal-title":"IJCAI"},{"key":"9418_CR24","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.neucom.2018.12.025","volume":"332","author":"Y Pan","year":"2019","unstructured":"Pan, Y., He, F., Yu, H.: A novel enhanced collaborative autoencoder with knowledge distillation for top-n recommender systems. Neurocomputing 332, 137\u2013148 (2019)","journal-title":"Neurocomputing"},{"key":"9418_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-019-8123-3","volume":"14","author":"Y Pan","year":"2020","unstructured":"Pan, Y., He, F., Yu, H.: A correlative denoising autoencoder to model social influence for top-n recommender system. Front. Comput. Sci. 14, 1\u201313 (2020)","journal-title":"Front. Comput. Sci."},{"key":"9418_CR26","doi-asserted-by":"publisher","first-page":"5453","DOI":"10.1109\/ACCESS.2023.3236391","volume":"11","author":"Y Lu","year":"2023","unstructured":"Lu, Y., Nakamura, K., Ichise, R.: Hyperrs: hypernetwork-based recommender system for the user cold-start problem. IEEE Access 11, 5453\u20135463 (2023)","journal-title":"IEEE Access"},{"key":"9418_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113054","volume":"144","author":"R Kiran","year":"2020","unstructured":"Kiran, R., Kumar, P., Bhasker, B.: Dnnrec: a novel deep learning based hybrid recommender system. Expert Syst. Appl. 144, 113054 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"9418_CR28","doi-asserted-by":"publisher","first-page":"2071","DOI":"10.1007\/s00500-022-07378-0","volume":"27","author":"SGK Patro","year":"2023","unstructured":"Patro, S.G.K., Mishra, B.K., Panda, S.K., Kumar, R., Long, H.V., Taniar, D.: Cold start aware hybrid recommender system approach for e-commerce users. Soft Computing 27(4), 2071\u20132091 (2023)","journal-title":"Soft Computing"},{"issue":"3","key":"9418_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560487","volume":"41","author":"D Cai","year":"2023","unstructured":"Cai, D., Qian, S., Fang, Q., Hu, J., Xu, C.: User cold-start recommendation via inductive heterogeneous graph neural network. ACM Trans. Inform. Syst. 41(3), 1\u201327 (2023)","journal-title":"ACM Trans. Inform. Syst."},{"key":"9418_CR30","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.ins.2020.05.071","volume":"536","author":"J Herce-Zelaya","year":"2020","unstructured":"Herce-Zelaya, J., Porcel, C., Bernab\u00e9-Moreno, J., Tejeda-Lorente, A., Herrera-Viedma, E.: New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. Inform. Sci. 536, 156\u2013170 (2020)","journal-title":"Inform. Sci."},{"key":"9418_CR31","doi-asserted-by":"publisher","first-page":"13768","DOI":"10.1109\/ACCESS.2022.3147955","volume":"10","author":"M Dadgar","year":"2022","unstructured":"Dadgar, M., Hamzeh, A.: How to boost the performance of recommender systems by social trust studying the challenges and proposing a solution. IEEE Access 10, 13768\u201313779 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3147955","journal-title":"IEEE Access"},{"key":"9418_CR32","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv\u00a0preprint arXiv:1301.3781 (2013)"},{"key":"9418_CR33","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.ins.2022.01.033","volume":"592","author":"S Lee","year":"2022","unstructured":"Lee, S., Kim, D.: Deep learning based recommender system using cross convolutional filters. Inform. Sci. 592, 112\u2013122 (2022)","journal-title":"Inform. Sci."},{"key":"9418_CR34","unstructured":"Shi, W., Caballero, J., Theis, L., Huszar, F., Aitken, A., Ledig, C., Wang, Z.: Is the deconvolution layer the same as a convolutional layer? ArXiv\u00a0Preprint ArXiv:1609.07009 (2016)"},{"key":"9418_CR35","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Sun, H., Takeuchi, M., Katto, J.: Deep convolutional autoencoder-based lossy image compression. In: 2018 Picture Coding Symposium (PCS), pp. 253\u2013257 (2018). IEEE","DOI":"10.1109\/PCS.2018.8456308"},{"key":"9418_CR36","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv\u00a0Preprint arXiv:1412.6980 (2014)"},{"issue":"4","key":"9418_CR37","first-page":"1","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. Acm Trans. Interact. Intell. Syst. (tiis) 5(4), 1\u201319 (2015)","journal-title":"Acm Trans. Interact. Intell. Syst. (tiis)"}],"container-title":["User Modeling and User-Adapted Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-024-09418-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11257-024-09418-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-024-09418-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T03:10:16Z","timestamp":1732504216000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11257-024-09418-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,23]]},"references-count":37,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["9418"],"URL":"https:\/\/doi.org\/10.1007\/s11257-024-09418-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4006730\/v1","asserted-by":"object"}]},"ISSN":["0924-1868","1573-1391"],"issn-type":[{"value":"0924-1868","type":"print"},{"value":"1573-1391","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,23]]},"assertion":[{"value":"2 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2024","order":3,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}