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- W4385285881 abstract "Factorization Machines (FMs) have been proven effective in modeling the second-order interactions between attributes. Nevertheless, existing methods often fall short of learning the complex and rich intra-attribute feature interactions, which are abundantly exhibited in real-world recommendation data. To address this problem, we propose an FM framework equipped with generalized metric learning to better handle the essential feature correlations. In particular, based on this framework, we present a Mahalanobis distance and a deep neural network method, which can effectively model the linear and non-linear correlations between features, respectively. Besides, we design an efficient approach for simplifying the model functions. Experiments on several benchmark datasets demonstrate that our proposed framework outperforms several state-of-the-art baselines by a significant margin." @default.
- W4385285881 created "2023-07-27" @default.
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- W4385285881 date "2023-04-01" @default.
- W4385285881 modified "2023-09-23" @default.
- W4385285881 title "Enhancing Factorization Machines with Generalized Metric Learning (Extended Abstract)" @default.
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- W4385285881 doi "https://doi.org/10.1109/icde55515.2023.00304" @default.
- W4385285881 hasPublicationYear "2023" @default.
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