How does overfitting affect the diversity and novelty of recommendations?

To address this issue, prior research has adopted two approaches: contrastive learning and information maximization. Contrastive-learning [10, 19, 34, 35, 14, ... Mar 15, 2024 ... We explain this finding with the privileged role of information provided by the taxonomic label: the modified head noun encodes the cognitive ... Nov 7, 2024 ... The application of principles derived from research on causality has benefited current learning models by constraining the search over the space ... For a new information criterion that utilizes quantified uncertainty to penalize overfitting models, please refer to Thanasutives, P., Morita, T., Numao, M., & ... Oct 11, 2023 ... ... informational content, it facilitates models to learn ... Resilience Against Overfitting: When mitigating the risk of overfitting ... Feb 1, 2024 ... Model selection techniques must appropriately balance the consequence of overfitting [9, 11, 12, 14]. Many more information criteria are ... Apr 20, 2012 ... We consider the accuracy of the density nowcasts as within-quarter information on monthly indicators accumulates. We focus on their ability to ... Even models which are not overfitted to the training data can still be vulnerable to privacy attacks. In cases where confidence information provided by a ML ... Feb 10, 2020 ... ... data over high-speed global networks, store data ... overfitting to patterns, and those that are specific to use of personal information. Nov 21, 2024 ... An Information-Theoretic Perspective on Overfitting and Underfitting ... We present an information-theoretic framework for understanding ...