{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T09:23:49Z","timestamp":1775381029720,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T00:00:00Z","timestamp":1615766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T00:00:00Z","timestamp":1615766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s11063-021-10475-0","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T08:02:53Z","timestamp":1615795373000},"page":"1865-1888","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Enhancing Top-N Recommendation Using Stacked Autoencoder in Context-Aware Recommender System"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7957-7934","authenticated-orcid":false,"given":"S.","family":"Abinaya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. K. Kavitha","family":"Devi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"issue":"6","key":"10475_CR1","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 (2005) 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","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"7","key":"10475_CR2","doi-asserted-by":"publisher","first-page":"2678","DOI":"10.1109\/TCYB.2018.2841924","volume":"49","author":"CD Wang","year":"2018","unstructured":"Wang CD, Deng ZH, Lai JH, Philip SY (2018) Serendipitous recommendation in e-commerce using innovator-based collaborative filtering. IEEE Trans Cybern 49(7):2678\u20132692","journal-title":"IEEE Trans Cybern"},{"key":"10475_CR3","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.eswa.2018.02.033","volume":"103","author":"T Kun","year":"2018","unstructured":"T., Kun, Shuyan C., and Aemal J. K. (2018) Personalized travel time estimation for urban road networks: a tensor-based context-aware approach. Expert Syst Appl 103:118\u2013132","journal-title":"Expert Syst Appl"},{"key":"10475_CR4","doi-asserted-by":"crossref","unstructured":"Macedo AQ, Marinho LB, Santos RL (2015) Context-aware event recommendation in event-based social networks. In: Proceedings of the 9th ACM conference on recommender-systems, pp 123\u2013130","DOI":"10.1145\/2792838.2800187"},{"key":"10475_CR5","doi-asserted-by":"crossref","unstructured":"Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Recommender systems-handbook. Springer, Boston, pp 217\u2013253","DOI":"10.1007\/978-0-387-85820-3_7"},{"key":"10475_CR6","doi-asserted-by":"crossref","unstructured":"Baltrunas L, Ricci F (2009) Context-based splitting of item ratings in collaborative filtering. In: Proceedings of the third ACM conference on recommender systems, pp 245\u2013248","DOI":"10.1145\/1639714.1639759"},{"key":"10475_CR7","doi-asserted-by":"crossref","unstructured":"Raghavan S, Gunasekar S, Ghosh J (2012) Review quality aware collaborative filtering. In: Proceedings of the sixth ACM conference on recommender systems, pp 123\u2013130","DOI":"10.1145\/2365952.2365978"},{"key":"10475_CR8","doi-asserted-by":"crossref","unstructured":"Cheng Z, Ding Y, Zhu L, Kankanhalli M (2018) Aspect-aware latent factor model: rating prediction with ratings and reviews. In: Proceedings of the 2018 world wide web conference, pp 639\u2013648","DOI":"10.1145\/3178876.3186145"},{"issue":"4","key":"10475_CR9","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1145\/1278366.1278372","volume":"7","author":"B Mobasher","year":"2007","unstructured":"Mobasher B, Burke R, Bhaumik R, Williams C (2007) Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Trans Internet Technol 7(4):23","journal-title":"ACM Trans Internet Technol"},{"key":"10475_CR10","doi-asserted-by":"crossref","unstructured":"Hart-Davidson W, Michael ML, Christopher K, Michael W (2010) A method for measuring helpfulness in online peer review. In: Proceedings of the 28th ACM international conference on design of communication, pp 115\u2013121","DOI":"10.1145\/1878450.1878470"},{"key":"10475_CR11","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhang H, Zhang M, Liu Y, Ma S (2014) Do users rate or review? Boost phrase-level sentiment labeling with review-level sentiment classification. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval, pp 1027\u20131030","DOI":"10.1145\/2600428.2609501"},{"issue":"2","key":"10475_CR12","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MIS.2013.30","volume":"28","author":"E Cambria","year":"2013","unstructured":"Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15\u201321","journal-title":"IEEE Intell Syst"},{"key":"10475_CR13","doi-asserted-by":"crossref","unstructured":"Pappas N, Popescu-Belis A (2013) Sentiment analysis of user comments for one-class collaborative filtering over ted talks. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval, pp 773\u2013776","DOI":"10.1145\/2484028.2484116"},{"key":"10475_CR14","doi-asserted-by":"crossref","unstructured":"Zhang Y, Lai G, Zhang M, Zhang Y, Liu Y, Ma S (2014) Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval, pp 83\u201392","DOI":"10.1145\/2600428.2609579"},{"key":"10475_CR15","doi-asserted-by":"crossref","unstructured":"Faridani S (2011) Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search. In: Proceedings of the fifth ACM conference on recommender systems, pp 355\u2013358","DOI":"10.1145\/2043932.2044005"},{"key":"10475_CR16","unstructured":"Ganu G, Elhadad N, Marian A (2009) Beyond the stars: improving rating predictions using review text content. In: WebDB, vol 9, pp 1\u20136"},{"key":"10475_CR17","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.ins.2018.04.009","volume":"451","author":"J Qiu","year":"2018","unstructured":"Qiu J, Liu C, Li Y, Lin Z (2018) Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Inf Sci 451:295\u2013309","journal-title":"Inf Sci"},{"key":"10475_CR18","doi-asserted-by":"crossref","unstructured":"Cen Y, Zou X, Zhang J, Yang H, Zhou J, Tang,J (2019) Representation learning for attributed multiplex heterogeneous network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1358\u20131368","DOI":"10.1145\/3292500.3330964"},{"key":"10475_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2019.11.095","volume":"385","author":"R Huang","year":"2020","unstructured":"Huang R, Wang N, Han C, Yu F, Cui L (2020) TNAM: a tag-aware neural attention model for Top-N recommendation. Neurocomputing 385:1\u201312","journal-title":"Neurocomputing"},{"issue":"2","key":"10475_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386243","volume":"11","author":"M Unger","year":"2020","unstructured":"Unger M, Tuzhilin A, Livne A (2020) Context-aware recommendations based on deep learning frameworks. ACM Trans Manag Inform Syst 11(2):1\u201315","journal-title":"ACM Trans Manag Inform Syst"},{"key":"10475_CR21","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.neunet.2020.01.008","volume":"124","author":"B Wu","year":"2020","unstructured":"Wu B, Wen W, Hao Z, Cai R (2020) Multi-context aware user-item embedding for recommendation. Neural Netw 124:86\u201394","journal-title":"Neural Netw"},{"key":"10475_CR22","doi-asserted-by":"crossref","unstructured":"Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on world wide web, pp 111\u2013112","DOI":"10.1145\/2740908.2742726"},{"key":"10475_CR23","doi-asserted-by":"crossref","unstructured":"Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining, pp 153\u2013162","DOI":"10.1145\/2835776.2835837"},{"key":"10475_CR24","doi-asserted-by":"crossref","unstructured":"Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235\u20131244","DOI":"10.1145\/2783258.2783273"},{"key":"10475_CR25","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"10475_CR26","unstructured":"Strub F, Mary J, Gaudel R (2016) Hybrid collaborative filtering with autoencoders. arXiv: 1603.00806"},{"issue":"2","key":"10475_CR27","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/s11063-018-9831-7","volume":"49","author":"M Wang","year":"2019","unstructured":"Wang M, Wu Z, Sun X, Feng G, Zhang B (2019) Trust-aware collaborative filtering with a denoising autoencoder. Neural Process Lett 49(2):835\u2013849","journal-title":"Neural Process Lett"},{"key":"10475_CR28","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.cogsys.2019.01.011","volume":"55","author":"K Wang","year":"2019","unstructured":"Wang K, Xu L, Huang L, Wang CD, Lai JH (2019) SDDRS: stacked discriminative denoising auto-encoder based recommender system. Cogn Syst Res 55:164\u2013174","journal-title":"Cogn Syst Res"},{"key":"10475_CR29","doi-asserted-by":"crossref","unstructured":"Dess\u00ec D, Dragoni M, Fenu G, Marras M, Recupero DR (2019) Evaluating neural word embeddings created from online course reviews for sentiment analysis. In: Proceedings of the 34th ACM\/SIGAPP symposium on applied computing, pp 2124\u20132127","DOI":"10.1145\/3297280.3297620"},{"key":"10475_CR30","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.eswa.2019.02.015","volume":"126","author":"TM Phuong","year":"2019","unstructured":"Phuong TM, Phuong ND (2019) Graph-based context-aware collaborative filtering. Expert Syst Appl 126:9\u201319","journal-title":"Expert Syst Appl"},{"issue":"1","key":"10475_CR31","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.ijhm.2008.06.011","volume":"28","author":"Q Ye","year":"2009","unstructured":"Ye Q, Law R, Gu B (2009) The impact of online user reviews on hotel room sales. Int J Hosp Manag 28(1):180\u2013182","journal-title":"Int J Hosp Manag"},{"issue":"4","key":"10475_CR32","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1145\/2436256.2436274","volume":"56","author":"R Feldman","year":"2013","unstructured":"Feldman R (2013) Techniques and applications for sentiment analysis. Commun ACM 56(4):82\u201389","journal-title":"Commun ACM"},{"key":"10475_CR33","unstructured":"Poria S, Gelbukh A, Agarwal B, Cambria E, Howard N (2014) Sentic demo: a hybrid concept-level aspect-based sentiment analysis toolkit. In: ESWC 2014"},{"issue":"2010","key":"10475_CR34","first-page":"627","volume":"2","author":"B Liu","year":"2010","unstructured":"Liu B (2010) Sentiment analysis and subjectivity. Handbook Nat Lang Process 2(2010):627\u2013666","journal-title":"Handbook Nat Lang Process"},{"key":"10475_CR35","unstructured":"Loria S (2017) TextBlob: simplified text processing [a Python (2 and 3) library for processing textual data]"},{"key":"10475_CR36","doi-asserted-by":"crossref","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, pp 1096\u20131103","DOI":"10.1145\/1390156.1390294"},{"key":"10475_CR37","doi-asserted-by":"crossref","unstructured":"Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the tenth ACM international conference on web search and data mining, pp 425\u2013434","DOI":"10.1145\/3018661.3018665"},{"key":"10475_CR38","doi-asserted-by":"crossref","unstructured":"Sarwar B, Karypis G, Konstan J, Riedl J (2001). Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web, pp 285\u2013295","DOI":"10.1145\/371920.372071"},{"issue":"15","key":"10475_CR39","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.1016\/j.ins.2007.02.036","volume":"177","author":"MG Vozalis","year":"2007","unstructured":"Vozalis MG, Margaritis KG (2007) Using SVD and demographic data for the enhancement of generalized collaborative filtering. Inf Sci 177(15):3017\u20133037","journal-title":"Inf Sci"},{"key":"10475_CR40","unstructured":"Scholkopf B, Platt J, Hofmann T (2006). Greedy layer-wise training of deep networks. In: International conference on neural information processing systems. MIT Press, pp 153\u2013160"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10475-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10475-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10475-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T15:18:51Z","timestamp":1629904731000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10475-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,15]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["10475"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10475-0","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,15]]},"assertion":[{"value":"26 February 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}