The scientific paper “Evaluation of requirements collection strategies for a constraint-based recommender system in a social e-learning platform” has been presented at the scienfitic community of CSEDU 2016, the International Conference on Computer Supported Education , by the italian startup Social Thingum, together with the Department of Computer Science of the University of Milano and the Deparment of Computer Science and Communication of the University of Milano Bicocca.
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.