A Multidimensional Paper Recommender: Experiments and Evaluations

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.

[1]  Mimi Recker,et al.  What do you recommend? Implementation and analyses of collaborative information filtering of web resources for education , 2003 .

[2]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[3]  Gordon I. McCalla,et al.  Utilizing Artificial Learners to Help Overcome the Cold-Start Problem in a Pedagogically-Oriented Paper Recommendation System , 2004, AH.

[4]  Ya Tang,et al.  The design and study of pedagogical paper recommendation , 2008 .

[5]  Allison Woodruff,et al.  Enhancing a digital book with a reading recommender , 2000, CHI.

[6]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[7]  C. Lee Giles,et al.  A system for automatic personalized tracking of scientific literature on the Web , 1999, DL '99.

[8]  Sean M. McNee,et al.  Being accurate is not enough: how accuracy metrics have hurt recommender systems , 2006, CHI Extended Abstracts.

[9]  Soo Young Rieh.  Judgment of information quality and cognitive authority in the Web , 2002, J. Assoc. Inf. Sci. Technol..

[10]  Gordon I. McCalla,et al.  On the Pedagogically Guided Paper Recommendation for an Evolving Web-Based Learning System , 2004, FLAIRS Conference.

[11]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[12]  George Lekakos,et al.  Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors , 2006, Interact. Comput..

[13]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[14]  AdomaviciusGediminas,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005 .

[15]  Sean M. McNee,et al.  On the recommending of citations for research papers , 2002, CSCW '02.