CL4CTR: A Contrastive Learning Framework for CTR Prediction

• URL: http://arxiv.org/abs/2212.00522v1
• Date: Thu, 1 Dec 2022 14:18:02 GMT
• Title: CL4CTR: A Contrastive Learning Framework for CTR Prediction
• Authors: Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu
• Abstract summary: We introduce self-supervised learning to produce high-quality feature representations directly. We propose a model-agnostic Contrastive Learning for CTR (CL4CTR) framework consisting of three self-supervised learning signals. CL4CTR achieves the best performance on four datasets.
• Score: 14.968714571151509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e.g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance. For instance, low frequency features, which account for the majority of features in many CTR tasks, are less considered in standard supervised learning settings, leading to sub-optimal feature representations. In this paper, we introduce self-supervised learning to produce high-quality feature representations directly and propose a model-agnostic Contrastive Learning for CTR (CL4CTR) framework consisting of three self-supervised learning signals to regularize the feature representation learning: contrastive loss, feature alignment, and field uniformity. The contrastive module first constructs positive feature pairs by data augmentation and then minimizes the distance between the representations of each positive feature pair by the contrastive loss. The feature alignment constraint forces the representations of features from the same field to be close, and the field uniformity constraint forces the representations of features from different fields to be distant. Extensive experiments verify that CL4CTR achieves the best performance on four datasets and has excellent effectiveness and compatibility with various representative baselines.

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