Recommender Systems for Technology Enhanced Learning ( RecSysTEL 2010 ) Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning

This paper raises the issue of missing data sets for recommender systems in Technology Enhanced Learning that can be used as benchmarks to compare different recommendation approaches. It discusses how suitable data sets could be created according to some initial suggestions, and investigates a number of steps that may be followed in order to develop reference data sets that will be adopted and reused within a scientific community. In addition, policies are discussed that are needed to enhance sharing of data sets by taking into account legal protection rights. Finally, an initial elaboration of a representation and exchange format for sharable TEL data sets is carried out. The paper concludes with future research needs.

[1]  Riina Vuorikari,et al.  Collaborative recommendation of e-learning resources: an experimental investigation , 2010, J. Comput. Assist. Learn..

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

[3]  Andreas Hotho,et al.  The anti-social tagger: detecting spam in social bookmarking systems , 2008, AIRWeb '08.

[4]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[5]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[6]  Ahmed K. Elmagarmid,et al.  Duplicate Record Detection: A Survey , 2007, IEEE Transactions on Knowledge and Data Engineering.

[7]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[8]  Lloyd Rutledge,et al.  ReMashed - An Usability Study of a Recommender System for Mash-Ups for Learning , 2010, iJET.

[9]  Xavier Ochoa,et al.  Bringing together what belongs together: a recommender-system to foster academic collaboration , 2008 .

[10]  Marc Najork,et al.  On near-uniform URL sampling , 2000, Comput. Networks.

[11]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[12]  Stephen B. Seidman,et al.  Network structure and minimum degree , 1983 .

[13]  Derry O'Sullivan,et al.  Improving Case-Based Recommendation: A Collaborative Filtering Approach , 2002, ECCBR.

[14]  D. Kirkpatrick.  Techniques for evaluating training programs , 1979 .

[15]  Rob Koper,et al.  Visualisation of Interaction Footprints for Engagement in Online Communities , 2009, J. Educ. Technol. Soc..

[16]  Nikos Manouselis,et al.  Experimental Analysis of Multiattribute Utility Collaborative Filtering on a Synthetic Data Set , 2008, Personalization Techniques and Recommender Systems.

[17]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[18]  A.P.J. van den Bosch,et al.  Recommender Systems for Social Bookmarking , 2005 .

[19]  Hendrik Drachsler,et al.  Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning , 2009, J. Digit. Inf..

[20]  Riina Vuorikari,et al.  What If Annotations Were Reusable: A Preliminary Discussion , 2009, ICWL.

[21]  Stefanie N. Lindstaedt,et al.  Getting to Know Your User - Unobtrusive User Model Maintenance within Work-Integrated Learning Environments , 2009, EC-TEL.

[22]  Hendrik Drachsler,et al.  Recommender Systems in Technology Enhanced Learning , 2011, Recommender Systems Handbook.

[23]  Erik Duval,et al.  Metadata for social recommendations: storing, sharing and reusing evaluations of learning resources , 2008 .