Laws of Attraction: In Search of Document Value-ness for Recommendation

In this paper we explore the uniqueness of paper recommendation for e-learning systems through a human-subject study. Experiment results showed that the majority of learners have struggled to reach a ‘harmony’ between their interest and educational goal: they admit that in order to acquire new knowledge, they are willing to read not-interesting-yet-pedagogically-useful papers. In other words, learners seem to be more tolerant than users in commercial recommender systems. Nevertheless, as educators, we should still maintain a balance of recommending interesting papers and pedagogically helpful ones in order to retain learners and continuously engage them throughout the learning process.

[1]  Lawrence B. Holder,et al.  Substructure Discovery Using Minimum Description Length and Background Knowledge , 1993, J. Artif. Intell. Res..

[2]  Andreas Paepcke,et al.  Beyond document similarity: understanding value-based search and browsing technologies , 2000, SGMD.

[3]  Gordon I. McCalla,et al.  Evaluating a Smart Recommender for an Evolving E-learning System: A Simulation-Based Study , 2004, Canadian Conference on AI.

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

[5]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[6]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

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

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

[9]  William W. Cohen,et al.  Recommendation : A Study in Combining Multiple Information Sources , 2007 .

[10]  Alfred Kobsa,et al.  Personalised hypermedia presentation techniques for improving online customer relationships , 2001, The Knowledge Engineering Review.

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

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

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

[14]  Riccardo Rizzo,et al.  Map-based horizontal navigation in educational Hypertext , 2002, HYPERTEXT '02.