Abstract
Factorization Machines (FMs) are extensively used for sparse contextual prediction tasks by modeling feature interactions. Despite successful application of FM and its abundant deep learning variants, these improved FMs mainly focus on capturing feature interaction at the vector-wise level while ignoring the more sophisticated bit-wise information. In this paper, we propose a novel Convolutional Feature-interacted Factorization Machine (CFFM), which learns crucial interactive patterns from enhanced feature-interacted maps with non-linearity. Specifically, in the high-order feature interactions part of CFFM, we propose a special Convolutional Max Pooling (Conv-MP) block to adequately learn interaction patterns from both vector-wise and bit-wise perspectives. Besides, we improve linear regression in FMs by incorporating a linear attention mechanism. Extensive experiments on two public datasets demonstrate that CFFM outperforms several state-of-the-art approaches.
Supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61672498.
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Huang, R., Han, C., Cui, L. (2021). Convolutional Feature-Interacted Factorization Machines for Sparse Contextual Prediction. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_48
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DOI: https://doi.org/10.1007/978-3-030-92270-2_48
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