This paper proposes a novel method for user profiling in recommender systems (RS). RS have emerged as a key tool in information filtering. But despite their importance in our lives, systems still suffer from the cold-start problem: the inability to infer preferences of a new user who has not rated enough items. Up till now, only limited research has focused on optimizing user profile acquisition processes. This paper addresses that gap, employing a gamified personalityacquisition system based on the widely used Five Factor Model (FFM) for assessing personality. Our web-based system accurately extrapolates a user’s preferences by guiding them through a series of interactive and contextualized questions. This paper demonstrates the efficacy of a gamified user profiling system that employs story-based questions derived from explicit personality inventory questions. The Gamified Personality Acquisition (GPA) system was shown to increase Mean Absolute Error (MAE) and Receiver Operating Characteristic (ROC) sensitivity in a travel RS while mitigating the cold-start problem in comparison to rating-based and traditional personality-based RS.
CITATION STYLE
Teklemicael, F., Zhang, Y., Wu, Y., Yin, Y., & Xing, C. (2016). Toward gamified personality acquisition in travel recommender systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9567, pp. 375–385). Springer Verlag. https://doi.org/10.1007/978-3-319-31854-7_34
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