A Multidimensional Paper Recommender: Experiments and Evaluations Export

@article{citeulike:5655358, abstract = {Paper recommender systems in the e-learning domain must consider pedagogical factors, such as a paper's overall popularity and learner background knowledge - factors that are less important in commercial book or movie recommender systems. This article reports evaluations of a 6D paper recommender. Experimental results from a human subject study of learner preferences suggest that pedagogical factors help to overcome a serious cold-start problem (not having enough papers or learners to start the recommender system) and help the system more appropriately support users as they learn.}, address = {Piscataway, NJ, USA}, author = {Tang, T. Y. and McCalla, G.}, citeulike-article-id = {5655358}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1592068}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/MIC.2009.73}, citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5167266}, day = {21}, doi = {10.1109/MIC.2009.73}, issn = {1089-7801}, journal = {Internet Computing, IEEE}, keywords = {academic-reference, dlpaws, recommender}, month = {July}, number = {4}, pages = {34--41}, posted-at = {2009-08-26 23:42:38}, priority = {2}, publisher = {IEEE Educational Activities Department}, title = {A Multidimensional Paper Recommender: Experiments and Evaluations}, url = {http://dx.doi.org/10.1109/MIC.2009.73}, volume = {13}, year = {2009} }

TY - JOUR ID - citeulike:5655358 L3 - citeulike-article-id:5655358 N2 - Paper recommender systems in the e-learning domain must consider pedagogical factors, such as a paper's overall popularity and learner background knowledge - factors that are less important in commercial book or movie recommender systems. This article reports evaluations of a 6D paper recommender. Experimental results from a human subject study of learner preferences suggest that pedagogical factors help to overcome a serious cold-start problem (not having enough papers or learners to start the recommender system) and help the system more appropriately support users as they learn. IS - 4 JF - Internet Computing, IEEE SN - 1089-7801 AD - Piscataway, NJ, USA EP - 41 TI - A Multidimensional Paper Recommender: Experiments and Evaluations PB - IEEE Educational Activities Department VL - 13 SP - 34 KW - academic-reference KW - dlpaws KW - recommender AU - Tang, TY AU - McCalla, G PY - 2009/07/21/ UR - http://dx.doi.org/10.1109/MIC.2009.73 ER -