Authors:
Francesco Epifania
1
and
Riccardo Porrini
2
Affiliations:
1
University of Milano, Social Things s.r.l. and University of Milano-Bicocca, Italy
;
2
University of Milano-Bicocca, Italy
Keyword(s):
Recommender System, Learning Resources, Social Network, e-Learning, User-centric Evaluation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
e-Learning
;
e-Learning Platforms
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Simulation and Modeling
;
Simulation Tools and Platforms
Abstract:
The NETT Recommender System (NETT-RS) is a constraint-based recommender system that recommends
learning resources to teachers who want to design courses. As for many state-of-the-art constraint-based
recommender systems, the NETT-RS bases its recommendation process on the collection of requirements to
which items must adhere in order to be recommended. In this paper we study the effects of two different
requirement collection strategies on the perceived overall recommendation quality of the NETT-RS. In the
first strategy users are not allowed to refine and change the requirements once chosen, while in the second
strategy the system allows the users to modify the requirements (we refer to this strategy as backtracking).
We run the study following the well established ResQue methodology for user-centric evaluation of RS. Our
experimental results indicate that backtracking has a strong positive impact on the perceived recommendation
quality of the NETT-RS.