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Research Topics for Quaternion Factorization Machines

Research Topics for Quaternion Factorization Machines

   Factorization machines (FM) is the groundbreaking technique of machine learning utilized to investigate feature interactions. FM models are the expansion of linear models designed to automatically learn and seize the interaction between features within high-dimensional scattered datasets. The significance of factorization machines is the ability of FM models to solve the problems with high order feature interaction effectively in terms of both time and space complexity. FM models are a supervised method that efficiently provides model feature interceptions and is widely used in the case of tasks such as interference prediction and applied in recommendation systems.
    The main advantage of FM models is the avoidance of high cost in task-specific feature engineering. Several deep neural network-based FM variants are developed to enhance the feature modeling and improve the predictive performance. DNN based FM variants face an issue of heavy parameterization in some real-life applications. The emergence of quaternion factorization machines (QFM) are designed to produce a lightweight solution for complex feature interaction modeling. QFM models can handle hyper-complex problems in modeling feature interactions based on reliable representations. Some of the successful applications of QGM are image processing, speech recognition, and text classification.