'That’s enough!' (but it wasn’t): the generative possibilities of attuning to what else a tantrum can do

Often used in the plural, tantrum denotes an uncontrolled outburst of anger and frustration, typically in a young child. In this paper we attempt to enact a feminist project of reclamation and reconfiguration of ‘the toddler tantrum’. Drawing on a range of theoretical traditions, this paper investigates the complex yet generative possibilities inherent within the tantrum to argue that it can be encountered as more-than-human, as a worldly-becoming, and as a form of resistance to Anthropocentrism and childism. We propose that the tantrum might be reappraised as a generative form of (child) activism. By mobilising the potential of arts-based approaches to the study of childhood we seek to reach other, opened out and speculative accounts of what tantrum-ing is, what it makes possible, and what it might offer to stretch ideas about, and practices with very young children. We undertake a tentacular engagement with children’s literature to arrive at possibilities to resist smoothing out, extinguishing or demonising the uncomfortable affective ecologies that are agitated by child rage. This paper brings together a concern with affect, materialities and bodies as they coalesce in more-than-human relationalities captured within ‘the tantrum’. In doing so, the unthinkable, the unbearable, the uncomfortable and the unknowable are set in motion, in the hope of arriving at a (more) critically affirmative account of childhood in all its messy complexity

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It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic

User interactions with recommender systems (RSs) are affected by user selection bias, e.g., users are more likely to rate popular items (popularity bias) or items that they expect to enjoy beforehand (positivity bias). Methods exist for mitigating the effects of selection bias in user ratings on the evaluation and optimization of RSs. However, these methods treat selection bias as static, despite the fact that the popularity of an item may change drastically over time and the fact that user preferences may also change over time. We focus on the age of an item and its effect on selection bias and user preferences. Our experimental analysis reveals that the rating behavior of users on the MovieLens dataset is better captured by methods that consider effects from the age of item on bias and preferences. We theoretically show that in a dynamic scenario in which both the selection bias and user preferences are dynamic, existing debiasing methods are no longer unbiased. To address this limitation, we introduce DebiAsing in the dyNamiC scEnaRio (DANCER), a novel debiasing method that extends the inverse propensity scoring debiasing method to account for dynamic selection bias and user preferences. Our experimental results indicate that DANCER improves rating prediction performance compared to debiasing methods that incorrectly assume that selection bias is static in a dynamic scenario. To the best of our knowledge, DANCER is the first debiasing method that accounts for dynamic selection bias and user preferences in RSs.Comment: WSDM 202