{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:28:18Z","timestamp":1754155698666,"version":"3.41.2"},"reference-count":42,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2017,4,3]],"date-time":"2017-04-03T00:00:00Z","timestamp":1491177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PROG"],"published-print":{"date-parts":[[2017,4,3]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Clustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations<\/jats:title>\n<jats:p>The methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This research provides evidence toward new recommendation methods directed toward LO repositories.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/prog-05-2016-0044","type":"journal-article","created":{"date-parts":[[2017,3,22]],"date-time":"2017-03-22T11:04:33Z","timestamp":1490180673000},"page":"35-51","source":"Crossref","is-referenced-by-count":2,"title":["A comparison among approaches for recommending learning objects through collaborative filtering algorithms"],"prefix":"10.1108","volume":"51","author":[{"given":"Henrique Lemos","family":"dos Santos","sequence":"first","affiliation":[]},{"given":"Cristian","family":"Cechinel","sequence":"additional","affiliation":[]},{"given":"Ricardo Matsumura","family":"Ara\u00fajo","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"unstructured":"Adomavicius, G., Manouselis, N. and Kwon, Y. (2011), \u201cRecommender systems handbook\u201d, in Ricci, F., Rokach, L., Shapira, B. and Kantor, B.P. (Eds), Multi-Criteria Recommender Systems, Springer, Boston, MA, pp. 769-803.","key":"key2020120807262929600_ref001"},{"issue":"2","key":"key2020120807262929600_ref002","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1109\/TCE.2008.4560154","article-title":"Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems","volume":"54","year":"2008","journal-title":"IEEE Transactions on Consumer Electronics"},{"unstructured":"Breese, J.S., Heckerman, D. and Kadie, C. (1998), \u201cEmpirical analysis of predictive algorithms for collaborative filtering\u201d, in Cooper, G.F. and Moral, S. (Eds), Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, pp. 43-52.","key":"key2020120807262929600_ref003"},{"year":"2005","first-page":"17","article-title":"Limited knowledge shilling attacks in collaborative filtering systems","key":"key2020120807262929600_ref004"},{"issue":"1","key":"key2020120807262929600_ref005","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1016\/j.compedu.2011.01.012","article-title":"Statistical profiles of highly-rated learning objects","volume":"57","year":"2011","journal-title":"Computers & Education"},{"issue":"1","key":"key2020120807262929600_ref006","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ipm.2012.07.004","article-title":"Evaluating collaborative filtering recommendations inside large learn ing object repositories","volume":"49","year":"2013","journal-title":"Information Processing & Management"},{"issue":"1","key":"key2020120807262929600_ref007","first-page":"94","article-title":"Mining models for automated quality assessment of learning objects","volume":"22","year":"2016","journal-title":"Journal of Universal Computer Science"},{"issue":"1","key":"key2020120807262929600_ref008","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1108\/00330331211296312","article-title":"Interlinking educational resources and the web of data: a survey of challenges and approaches","volume":"47","year":"2013","journal-title":"Program"},{"issue":"4","key":"key2020120807262929600_ref009","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1504\/IJLT.2008.019376","article-title":"Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model","volume":"3","year":"2008","journal-title":"International Journal of Learning Technology"},{"year":"2010","first-page":"2849","article-title":"Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning","key":"key2020120807262929600_ref010"},{"unstructured":"Ge, M., Delgado-Battenfeld, C. and Jannach, D. (2010), \u201cBeyond accuracy: evaluating recommender systems by coverage and serendipity\u201d, Proceedings of the Fourth ACM Conference on Recommender Systems, ACM, Barcelona, pp. 257-260, available at: http:\/\/dl.acm.org\/citation.cfm?id=1864708","key":"key2020120807262929600_ref011"},{"issue":"6","key":"key2020120807262929600_ref012","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s11423-010-9155-4","article-title":"Learning materials recommendation using good learners\u2019 ratings and content-based filtering","volume":"58","year":"2010","journal-title":"Educational Technology Research and Development"},{"issue":"1","key":"key2020120807262929600_ref013","first-page":"35","article-title":"Recommendation and students authoring in repositories of learning objects: a case-based reasoning approach","volume":"4","year":"2009","journal-title":"International Journal of Emerging Technologies in Learning"},{"year":"2009","first-page":"149","article-title":"Joining user clustering and item based collaborative filtering in personalized recommendation services","key":"key2020120807262929600_ref014"},{"unstructured":"Gonz\u00e1lez, D., Motz, R. and Tansini, L. (2015), \u201cOn the move to meaningful internet systems: OTM 2015 workshops\u201d, in Ciuciu, I., Panetto, H., Debruyne, C., Aubry, A., Bollen, P., Valencia-Garcia, R., Mishra, A., Fensel, A. and Ferri, F. (Eds), Improving Social Collaborations in Virtual Learning Environments, Springer International Publishing, Cham, pp. 528-535.","key":"key2020120807262929600_ref015"},{"issue":"1","key":"key2020120807262929600_ref016","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"},{"volume-title":"A Handbook of Statistical Analyses Using R","year":"2014","key":"key2020120807262929600_ref017"},{"issue":"3","key":"key2020120807262929600_ref018","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1016\/j.compedu.2010.11.001","article-title":"E-learning personalization based on hybrid recommendation strategy and learning style identification","volume":"56","year":"2011","journal-title":"Computers & Education"},{"doi-asserted-by":"crossref","unstructured":"Lehman, R. 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