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Gustavo F. Tondello,
Rita Orji, and
Lennart E. Nacke. 2017. Recommender Systems for Personalized Gamification. In
Proceedings of UMAP’17 Adjunct. Bratislava, Slovakia. ACM.
doi:10.1145/3099023.3099114 @inproceedings{Tondello2017a,
abstract = {Gamification has been used in a variety of application domains to promote behaviour change. Nevertheless, the mechanisms behind it are still not fully understood. Recent empirical results have shown that personalized approaches can potentially achieve better results than generic approaches. However, we still lack a general framework for building personalized gameful applications. To address this gap, we present a novel general framework for personalized gameful applications using recommender systems (i.e., software tools and technologies to recommend suggestions to users that they might enjoy). This framework contributes to understanding and building effective persuasive and gameful applications by describing the different building blocks of a recommender system (users, items, and transactions) in a personalized gamification context.},
address = {Bratislava, Slovakia},
author = {Tondello, Gustavo F. and Orji, Rita and Nacke, Lennart E.},
booktitle = {Proceedings of UMAP'17 Adjunct},
doi = {10.1145/3099023.3099114},
isbn = {9781450350679},
keywords = {Gamification,Personalization,Recommender Systems},
publisher = {ACM},
title = {{Recommender Systems for Personalized Gamification}},
year = {2017}
}Abstract
Gamification has been used in a variety of application domains to promote behaviour change. Nevertheless, the mechanisms behind it are still not fully understood. Recent empirical results have shown that personalized approaches can potentially achieve better results than generic approaches. However, we still lack a general framework for building personalized gameful applications. To address this gap, we present a novel general framework for personalized gameful applications using recommender systems (i.e., software tools and technologies to recommend suggestions to users that they might enjoy). This framework contributes to understanding and building effective persuasive and gameful applications by describing the different building blocks of a recommender system (users, items, and transactions) in a personalized gamification context.
