%0 Book Section %1 citeulike:13367443 %A Fazeli, Soude %A Loni, Babak %A Drachsler, Hendrik %A Sloep, Peter %B Open Learning and Teaching in Educational Communities %D 2014 %E Rensing, Christoph %E de Freitas, Sara %E Ley, Tobias %E Mu\ noz Merino, PedroJ %I Springer International Publishing %K personalized-learning recommender social-network %P 84--97 %R 10.1007/978-3-319-11200-8_7 %T Which Recommender System Can Best Fit Social Learning Platforms? %U http://dx.doi.org/10.1007/978-3-319-11200-8_7 %V 8719 %X This study aims to develop a recommender system for social learning platforms that combine traditional learning management systems with commercial social networks like Facebook. We therefore take into account social interactions of users to make recommendations on learning resources. We propose to make use of graph-walking methods for improving performance of the well-known baseline algorithms. We evaluate the proposed graph-based approach in terms of their F1 score, which is an effective combination of precision and recall as two fundamental metrics used in recommender systems area. The results show that the graph-based approach can help to improve performance of the baseline recommenders; particularly for rather sparse educational datasets used in this study.

@incollection{citeulike:13367443, abstract = {{This study aims to develop a recommender system for social learning platforms that combine traditional learning management systems with commercial social networks like Facebook. We therefore take into account social interactions of users to make recommendations on learning resources. We propose to make use of graph-walking methods for improving performance of the well-known baseline algorithms. We evaluate the proposed graph-based approach in terms of their F1 score, which is an effective combination of precision and recall as two fundamental metrics used in recommender systems area. The results show that the graph-based approach can help to improve performance of the baseline recommenders; particularly for rather sparse educational datasets used in this study.}}, added-at = {2018-03-19T12:24:51.000+0100}, author = {Fazeli, Soude and Loni, Babak and Drachsler, Hendrik and Sloep, Peter}, biburl = {https://www.bibsonomy.org/bibtex/2dda10ac7d5317a06052c17c6e3b71bb9/aho}, booktitle = {Open Learning and Teaching in Educational Communities}, citeulike-article-id = {13367443}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-319-11200-8_7}, citeulike-linkout-1 = {http://link.springer.com/chapter/10.1007/978-3-319-11200-8_7}, doi = {10.1007/978-3-319-11200-8_7}, editor = {Rensing, Christoph and de Freitas, Sara and Ley, Tobias and Mu\ {n}oz Merino, PedroJ}, interhash = {cf00905461f056e39fc18f9c9e197382}, intrahash = {dda10ac7d5317a06052c17c6e3b71bb9}, keywords = {personalized-learning recommender social-network}, pages = {84--97}, posted-at = {2014-09-19 10:25:29}, priority = {2}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Which Recommender System Can Best Fit Social Learning Platforms?}}, url = {http://dx.doi.org/10.1007/978-3-319-11200-8_7}, volume = 8719, year = 2014 }