Work

Dataset-driven research for improving recommender systems for learning

Year: 2011

Type: conference-paper

Abstract: In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or Each-Movie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for learning. We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to improve the performance of recommendation algorithms.

Source: International Conference on Learning Analytics and Knowledge (LAK)

DOI: 10.1145/2090116.2090122

Authors: Katrien Verbert, Hendrik Drachsler, Nikos Manouselis, Martin Wolpers, Riina Vuorikari, Erik Duval

Institutions: KU Leuven, Open University of the Netherlands, Universidad de Alcalá, Fraunhofer Institute for Applied Information Technology

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Fonds Wetenschappelijk Onderzoek, Vlaamse regering, Seventh Framework Programme

Award: G2010836076