{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:02:11Z","timestamp":1778716931774,"version":"3.51.4"},"reference-count":41,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T00:00:00Z","timestamp":1542067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["OIR"],"published-print":{"date-parts":[[2018,11,13]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user\u2019s occupation information to enhance recommendation accuracy.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>To improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/oir-02-2017-0069","type":"journal-article","created":{"date-parts":[[2018,11,12]],"date-time":"2018-11-12T10:03:17Z","timestamp":1542016997000},"page":"455-476","source":"Crossref","is-referenced-by-count":2,"title":["Context-aware restricted Boltzmann machine meets collaborative filtering"],"prefix":"10.1108","volume":"44","author":[{"given":"Jingshuai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanxin","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weizhu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenge","family":"Rong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"3","key":"key2020062510440591100_ref001","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1016\/j.eswa.2014.09.017","article-title":"A systematic review of scholar context-aware recommender systems","volume":"42","year":"2015","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"key2020062510440591100_ref002","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.eswa.2014.09.016","article-title":"RecomMetz: a context-aware knowledge-based mobile recommender system for movie showtimes","volume":"42","year":"2015","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"key2020062510440591100_ref003","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11263-013-0669-1","article-title":"The shape Boltzmann machine: a strong model of object shape","volume":"107","year":"2014","journal-title":"International Journal of Computer Vision"},{"key":"key2020062510440591100_ref004","first-page":"1148","article-title":"A non-IID framework for collaborative filtering with restricted Boltzmann machines","year":"2013","journal-title":"Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA"},{"key":"key2020062510440591100_ref005","unstructured":"Gr\u010dar, M., Mladeni\u010d, D., Fortuna, B. and Grobelnik, M. (2005), \u201cData sparsity issues in the collaborative filtering framework\u201d, Proceedings of 7nd International Workshop on Knowledge Discovery on the Web, Springer, Chicago, IL, pp. 58-76."},{"issue":"3","key":"key2020062510440591100_ref006","first-page":"57","article-title":"Merging trust in collaborative filtering to alleviate data sparsity and cold start","volume":"57","year":"2014","journal-title":"Knowledge-Based Systems"},{"key":"key2020062510440591100_ref007","first-page":"123","article-title":"TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings","year":"2015","journal-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence in Austin, TX"},{"key":"key2020062510440591100_ref008","first-page":"1","article-title":"Performance analysis of recommendation system based on collaborative filtering and demographics","year":"2015"},{"issue":"4","key":"key2020062510440591100_ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2827872","article-title":"The movielens datasets: history and context","volume":"5","year":"2016","journal-title":"ACM Transactions on Interactive Intelligent Systems"},{"issue":"1","key":"key2020062510440591100_ref010","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/963770.963772","article-title":"Evaluating collaborative filtering recommender systems","volume":"22","year":"2004","journal-title":"ACM Transactions on Information Systems"},{"key":"key2020062510440591100_ref011","first-page":"599","article-title":"A practical guide to training restricted Boltzmann machines","year":"2012","journal-title":"Neural Networks: Tricks of the Trade"},{"key":"key2020062510440591100_ref012","first-page":"349","article-title":"Context-aware recommender systems 2011","year":"2011"},{"key":"key2020062510440591100_ref013","article-title":"Advances in collaborative filtering","volume-title":"Recommender Systems Handbook","year":"2015"},{"issue":"8","key":"key2020062510440591100_ref014","first-page":"847","article-title":"Novel neighbor selection method to improve data sparsity problem in collaborative filtering","volume":"9","year":"2013","journal-title":"International Journal of Distributed Sensor Networks"},{"issue":"6","key":"key2020062510440591100_ref015","first-page":"1","article-title":"Scalable learning of probabilistic latent models for collaborative filtering","volume":"74","year":"2015","journal-title":"Decision Support Systems"},{"key":"key2020062510440591100_ref016","first-page":"811","article-title":"Deep collaborative filtering via marginalized denoising auto-encoder","year":"2015"},{"key":"key2020062510440591100_ref017","first-page":"609","article-title":"Item category aware conditional restricted boltzmann machine based recommendation","year":"2015"},{"issue":"6","key":"key2020062510440591100_ref018","first-page":"72","article-title":"Web mining based framework for solving usual problems in recommender systems: a case study for movies\u2019 recommendation","volume":"176","year":"2016","journal-title":"Neurocomputing"},{"key":"key2020062510440591100_ref019","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","year":"2010"},{"key":"key2020062510440591100_ref020","doi-asserted-by":"crossref","unstructured":"Nguyen, V.-D. and Huynh, V.-N. (2014), \u201cA community-based collaborative filtering system dealing with sparsity problem and data imperfections\u201d, Proceedings of the 13th Pacific Rim International Conference on Artificial Intelligence in Gold Coast, Springer, QLD, pp. 884-890.","DOI":"10.1007\/978-3-319-13560-1_74"},{"key":"key2020062510440591100_ref021","doi-asserted-by":"crossref","unstructured":"Ouyang, Y., Liu, W., Rong, W. and Xiong, Z. (2014), \u201cAutoencoder-based collaborative filtering\u201d, Proceedings of the 21st International Conference on Neural Information Processing, Springer, Kuching, pp. 284-291.","DOI":"10.1007\/978-3-319-12643-2_35"},{"key":"key2020062510440591100_ref022","doi-asserted-by":"crossref","unstructured":"Ouyang, Y., Zhang, J., Xie, W., Rong, W. and Xiong, Z. (2016), \u201cImplicit and explicit trust in collaborative filtering\u201d, Proceedings of the 9th International Conference on Knowledge Science, Engineering and Management, Springer, Passau, pp. 489-500.","DOI":"10.1007\/978-3-319-47650-6_39"},{"issue":"1","key":"key2020062510440591100_ref023","first-page":"23","article-title":"Adaptive sentiment-aware one-class collaborative filtering","volume":"43","year":"2016","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"key2020062510440591100_ref024","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1162\/neco.2008.04-07-510","article-title":"Representational power of restricted Boltzmann machines and deep belief networks","volume":"20","year":"2008","journal-title":"Neural Computation"},{"key":"key2020062510440591100_ref025","doi-asserted-by":"crossref","unstructured":"Salakhutdinov, R., Mnih, A. and Hinton, G. (2007), \u201cRestricted Boltzmann machines for collaborative filtering\u201d, Proceedings of the 24th International Conference on Machine Learning, ACM, Corvallis, OR, pp. 791-798.","DOI":"10.1145\/1273496.1273596"},{"key":"key2020062510440591100_ref026","doi-asserted-by":"crossref","unstructured":"Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2000), \u201cAnalysis of recommendation algorithms for e-commerce\u201d, Proceedings of the 2nd ACM Conference on Electronic Commerce, ACM, Minneapolis, MN, pp. 158-167.","DOI":"10.1145\/352871.352887"},{"key":"key2020062510440591100_ref027","first-page":"291","volume-title":"The Adaptive Web, Methods and Strategies of Web Personalization","year":"2007"},{"key":"key2020062510440591100_ref028","volume-title":"Social Network-Based Recommender Systems","year":"2015"},{"key":"key2020062510440591100_ref029","doi-asserted-by":"crossref","unstructured":"Sedhain, S., Menon, A.K., Sanner, S. and Xie, L. (2015), \u201cAutorec: autoencoders meet collaborative filtering\u201d, Proceedings of the 24th International Conference on World Wide Web, ACM, Florence, pp. 111-112.","DOI":"10.1145\/2740908.2742726"},{"key":"key2020062510440591100_ref030","first-page":"47","article-title":"Personalised rating prediction for new users using latent factor models","year":"2011"},{"key":"key2020062510440591100_ref031","first-page":"623","article-title":"Scalable collaborative filtering approaches for large recommender systems","volume":"10","year":"2009","journal-title":"Journal of Machine Learning Research"},{"key":"key2020062510440591100_ref032","first-page":"548","article-title":"Ordinal Boltzmann machines for collaborative filtering","year":"2009","journal-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, QC"},{"key":"key2020062510440591100_ref033","first-page":"2643","article-title":"Deep content-based music recommendation","year":"2013"},{"issue":"15","key":"key2020062510440591100_ref034","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1016\/j.ins.2007.02.036","article-title":"Using SVD and demographic data for the enhancement of generalized collaborative filtering","volume":"177","year":"2007","journal-title":"Information Sciences"},{"key":"key2020062510440591100_ref035","first-page":"3052","article-title":"Relational stacked denoising autoencoder for tag recommendation","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, TX","year":"2015"},{"key":"key2020062510440591100_ref036","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, N. and Yeung, D.-Y. (2015b), \u201cCollaborative deep learning for recommender systems\u201d, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Sydney, NSW, pp. 1235-1244.","DOI":"10.1145\/2783258.2783273"},{"key":"key2020062510440591100_ref037","first-page":"415","article-title":"Collaborative recurrent autoencoder: recommend while learning to fill in the blanks","year":"2016"},{"key":"key2020062510440591100_ref038","doi-asserted-by":"crossref","unstructured":"Wang, X. and Wang, Y. (2014), \u201cImproving content-based and hybrid music recommendation using deep learning\u201d, Proceedings of the ACM International Conference on Multimedia, ACM, Orlando, FL, pp. 627-636.","DOI":"10.1145\/2647868.2654940"},{"key":"key2020062510440591100_ref039","first-page":"454","article-title":"User occupation aware conditional restricted Boltzmann machine based recommendation","year":"2016"},{"key":"key2020062510440591100_ref040","first-page":"1633","article-title":"Social collaborative filtering by trust","year":"2017"},{"key":"key2020062510440591100_ref041","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Ouyang, Y., Rong, W. and Xiong, Z. (2015), \u201cMulti-faceted distrust aware recommendation\u201d, Proceedings of the 8th International Conference on Knowledge Science, Engineering and Management, Springer, Chongqing, pp. 435-446.","DOI":"10.1007\/978-3-319-25159-2_39"}],"container-title":["Online Information Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/OIR-02-2017-0069\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/OIR-02-2017-0069\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:42:14Z","timestamp":1753396934000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/oir\/article\/44\/2\/455-476\/320840"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,13]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,11,13]]}},"alternative-id":["10.1108\/OIR-02-2017-0069"],"URL":"https:\/\/doi.org\/10.1108\/oir-02-2017-0069","relation":{},"ISSN":["1468-4527"],"issn-type":[{"value":"1468-4527","type":"print"}],"subject":[],"published":{"date-parts":[[2018,11,13]]}}}