PRESENTAZIONE ALLA CSEDU 2016
20 aprile 2016
E’ stato presentato l’articolo scientifico “Evaluation of requirements collection strategies for a constraint-based recommender system in a social e-learning platform” alla comunità scientifica di CSEDU 2016, the International Conference on Computer Supported Education , dalla start up Social Thingum già Social Things srl insieme al Dipartimento di Informatica dell’Università degli Studi di Milano e al Dipartimento di Informatica Sistemistica e comunicazione dell’Università degli studi di Milano-Bicocca.
Ecco le foto e l’abstract del l’articolo che è stato pubblicato dopo una selettiva peer review.
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.
Ecco le foto e l’abstract del l’articolo che è stato pubblicato dopo una selettiva peer review.
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.
