UMAP 2019 Workshop on Explainable and Holistic User Modeling (ExHUM) Chairs' Welcome & Organization

C. Musto, A. Rapp, F. Cena, F. Hopfgartner, J. Kay, A. Lawlor, P. Lops, G. Semeraro, N. Tintarev
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Abstract

It is our great pleasure to welcome you to the UMAP 2019 Workshop on Explainable and Holisitic User Modeling (ExHUM). Our workshop took inspiration from the analysis of the recent Web dynamics: according to a recent claim by IBM, 90% of the data available today have been created in the last two years. Such an exponential growth of personal information has given new life to research in the area of user modelling, since information about users' preferences, sentiment and opinions, as well as signals describing their physical and psychological state, can now be obtained by mining data gathered from many heterogeneous sources. How can we use such data to drive personalization and adaptation mechanisms? How can we effectively merge such data to obtain a holistic representation of all (or some of) the facets describing people? Moreover, as the importance of such technologies in our everyday lives grows, it is also fundamental that the internal mechanisms that guide personalization algorithms are as clear as possible. It is not by chance that the recent General Data Protection Regulation (GDPR) emphasized the users' right to explanation when people face machine learning-based systems. Unfortunately, the current research tends to go in the opposite direction, since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of the explainability and the transparency of the model.
UMAP 2019可解释和整体用户建模(ExHUM)研讨会主席的欢迎和组织
我们非常高兴地欢迎您参加UMAP 2019年可解释和整体用户建模(ExHUM)研讨会。我们的研讨会从最近的Web动态分析中获得了灵感:根据IBM最近的声明,今天可用的数据中有90%是在过去两年中创建的。这种个人信息的指数级增长给用户建模领域的研究带来了新的生命,因为关于用户的偏好、情绪和意见的信息,以及描述他们的身体和心理状态的信号,现在可以通过挖掘从许多异构来源收集的数据来获得。我们如何使用这些数据来推动个性化和适应机制?我们如何有效地合并这些数据,以获得描述人的所有(或某些)方面的整体表示?此外,随着这些技术在我们日常生活中的重要性越来越大,引导个性化算法的内部机制尽可能清晰也是至关重要的。最近出台的《通用数据保护条例》(GDPR)强调了用户在面对基于机器学习的系统时的解释权,这并非偶然。不幸的是,目前的研究倾向于相反的方向,因为大多数方法都试图以牺牲模型的可解释性和透明度为代价,最大化个性化策略的有效性(例如,推荐准确性)。
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