Sign up & Download
Sign in

Dataset-driven Research for Improving TEL Recommender Systems

by Katrien Verbert, Erik Duval, Hendrik Drachsler, Nikos Manouselis, Martin Wolpers, Riina Vuorikari, Guenter Beham
1st International Conference on Learning Analytics and Knowledge ()

Abstract

In the world of recommender systems, it is a common prac- tice to use public available data sets from different application environ- ments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to eval- uate recommendation algorithms. These data sets are used as bench- marks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore data sets that capture learner interactions with tools and resources and that can be used to evaluate and compare the performance of different rec- ommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collabo- rative filtering algorithms applied to these TEL data sets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence and used to improve the perfor- mance of available recommendation algorithms.

Cite this document (BETA)

Authors on Mendeley

Readership Statistics

8 Readers on Mendeley
by Discipline
 
 
by Academic Status
 
38% Ph.D. Student
 
25% Assistant Professor
 
25% Post Doc
by Country
 
25% Belgium
 
13% Netherlands
 
13% Slovenia

Tags

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Already have an account? Sign in