Saudi Cultural Missions Theses & Dissertations
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Item Restricted MFExplain: An Interactive Tool for Explaining Movie Recommendations Generated with Matrix Factorization(University of Zurich, 2023-09-29) Alahmadi, Turki; Bernard, Jürgen; Al Hazwani, IbrahimRecommender systems have become integral in guiding users through the overwhelming abundance of online content. As these systems assume an ever-increasing role in shaping user decisions and preferences, there is a growing demand for clarity in their decision-making processes to instill trust. Recommendation algorithms with a high degree of accuracy such as matrix factorization are highly regarded and widely adopted. Nonetheless, these algorithms tend to exhibit high complexity in their logic and architecture, rendering them challenging to explain to end-users. This issue has been recognized and many tools have presented possible solutions. Many of the implemented approaches, however, have demonstrated shortcomings due to disregarding some user-centered properties or overly concentrating on unraveling the underlying algorithmic intricacy. This work presents MFExplain, an innovative tool for explaining movie recommendations generated with matrix factorization. The tool aims to explain recommendations by relying on the provision of intuitive justifications. Leveraging interactivity and cutting-edge dimensionality reduction techniques enables the tool to also encourage exploration, allow user feedback, and foster many desirable recommender system properties that enrich the user experience.42 0Item Restricted Information Integrity: From a Lens of Explainable AI With Cultural and Social Behaviors(2023-08-11) Alharbi, Raed; Thai, My TThe rapid development of Artificial intelligence (AI), such as machine learning (ML) and deep neural networks (DNNs), has changed the way information is processed and used. However, along with these advancements, challenges to information integrity have emerged. The widespread dissemination of misinformation through digital platforms, coupled with the lack of transparency in black-box ML models, has raised concerns about the reliability and trustworthiness of informa- tion to expert users (ML developers) and non-expert users (end-users). Unfortunately, employing eXplainable Artificial Intelligence (XAI) approaches on real-world applications to improve the trustworthiness of DNNs models is still far-fetched and not straightforward. Motivated by these observations, this thesis concentrates on two directions. • Misinformation Mitigation. In the first direction, we leverage XAI techniques to mitigate misinformation through three main approaches: evaluating the trustworthiness of fake news detection models from a user perspective, studying the influence of social and cultural behavior on misinformation propa- gation, and analyzing the diffusion of descriptive norms in social media networks to promote positive norms and combat misinformation. • Developing Advanced ML Models. In the second direction, we turn our attention to developing ML models from two aspects. The first aspect exploits XAI behaviors to provide a new method to simultaneously preserve the performance and explainability of student models, which in their primitive form provide little transparency. In the second aspect, we develop the Temporal graph Fake News Detec- tion Framework (T-FND), which effectively captures heterogeneous and repetitive charac- teristics of fake news behavior.22 0