Abstract
Click-Through-Rate (CTR) is a fundamental metric used to assess the efficacy of recommendation systems. In the past, most CTR prediction approaches focused on modeling the cross feature of various feature fields to improve the accuracy of CTR prediction. But they only learned the fixed representation of feature and neglected the varying significance of different feature fields in distinct contexts - what we refer to as context sensitive information - leading to suboptimal performance. While recent approaches have attempted to leverage linear transformations and feature interactions to capture context sensitive information, they remain inadequate as they overlook the varying importance of original features or different order cross features. In this paper, we propose a new module called Enhancing Feature Network (EFNet). EFNet has two key components: 1) Information Capture Layer (ICL), that dynamically captures explicit and implicit context sensitive information from original embedding features and digs out their corresponding bit-level weights; 2) Enhancing Feature Layer (EFL) that adaptively combine the context sensitive information with original embedding features according to the weights obtained in ICL. It is worth noting that EFNet can be integrated into existing CTR prediction models as a module to boost their overall performance. We conduct comprehensive experiments on four public datasets and the results demonstrate that models incorporating the EFNet module outperform other state-of-the-art models.
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This research is supported by the Research Found for Talented Scholars of Hebei University under the grant No. 521100221088.
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Liu, H., Guo, Y., Wang, L., Song, X. (2023). Feature Representation Enhancing by Context Sensitive Information in CTR Prediction. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14176. Springer, Cham. https://doi.org/10.1007/978-3-031-46661-8_46
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DOI: https://doi.org/10.1007/978-3-031-46661-8_46
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