Cited By
View all- Zhou JChang CLi WLin RWu ZTang Y(2024)SS4CTR: a semi-supervised framework for enhancing click-through rate prediction in sparse and imbalanced dataWorld Wide Web10.1007/s11280-024-01310-227:6Online publication date: 10-Oct-2024
CETN: Contrast-enhanced Through Network for Click-Through Rate Prediction
Article No.: 4, Pages 1 - 34
Abstract
Click-through rate (CTR) prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most existing CTR Prediction models utilize explicit feature interactions to overcome the performance bottleneck of implicit feature interactions. Hence, deep CTR models based on parallel structures (e.g., DCN, FinalMLP, xDeepFM) have been proposed to obtain joint information from different semantic spaces. However, these parallel subcomponents lack effective supervision and communication signals, making it challenging to efficiently capture valuable multi-views feature interaction information in different semantic spaces. To address these issues, we propose a simple yet effective novel CTR model: Contrast-enhanced Through Network (CETN). Drawing inspiration from sociology, CETN leverages the complementary nature of diversity and homogeneity to guide the model in acquiring higher-quality feature interaction information. Specifically, CETN employs product-based feature interactions and the augmentation (perturbation) concept from contrastive learning to segment different semantic spaces, each with distinct activation functions. This improves diversity in the feature interaction information captured by the model. Additionally, we introduce self-supervised signals and through connection within each semantic space to ensure the homogeneity of the captured feature interaction information. The experiments conducted on four real datasets demonstrate that our model consistently outperforms twenty baseline models in terms of AUC and Logloss.
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Index Terms
- CETN: Contrast-enhanced Through Network for Click-Through Rate Prediction
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Publication History
Published: 27 November 2024
Online AM: 12 August 2024
Accepted: 06 August 2024
Revised: 28 June 2024
Received: 21 March 2024
Published in TOIS Volume 43, Issue 1
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View all- Zhou JChang CLi WLin RWu ZTang Y(2024)SS4CTR: a semi-supervised framework for enhancing click-through rate prediction in sparse and imbalanced dataWorld Wide Web10.1007/s11280-024-01310-227:6Online publication date: 10-Oct-2024
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