Representation Learning-Assisted Click-Through Rate Prediction (1906.04365v3)

Abstract: Click-through rate (CTR) prediction is a critical task in online advertising systems. Most existing methods mainly model the feature-CTR relationship and suffer from the data sparsity issue. In this paper, we propose DeepMCP, which models other types of relationships in order to learn more informative and statistically reliable feature representations, and in consequence to improve the performance of CTR prediction. In particular, DeepMCP contains three parts: a matching subnet, a correlation subnet and a prediction subnet. These subnets model the user-ad, ad-ad and feature-CTR relationship respectively. When these subnets are jointly optimized under the supervision of the target labels, the learned feature representations have both good prediction powers and good representation abilities. Experiments on two large-scale datasets demonstrate that DeepMCP outperforms several state-of-the-art models for CTR prediction.

• The paper introduces DeepMCP, which integrates matching, correlation, and prediction subnets to enhance CTR prediction.
• The model employs multi-faceted representation learning to address data sparsity by capturing user-ad and ad-ad relationships.
• Experimental results on large-scale datasets, including Alibaba data, demonstrate improved AUC scores and lower log loss compared to baselines.

Representation Learning-Assisted Click-Through Rate Prediction: An Analytical Overview

The paper presents a comprehensive approach to enhancing click-through rate (CTR) prediction in online advertising systems, which is a pivotal component for optimizing ad placement and maximizing revenue. Traditional models often encounter limitations due to data sparsity and predominantly focus on the direct relationship between features and CTR. To address these issues, the authors introduce DeepMCP (Deep Matching, Correlation, and Prediction), a novel model that explores various feature relationships through multi-faceted learning strategies.

DeepMCP Model Architecture

DeepMCP is structured into three distinct subnets: the matching subnet, the correlation subnet, and the prediction subnet. Each of these subnets is designed to target specific relationships among the features involved in CTR prediction:

1. Matching Subnet: This component models the user-ad relationship, leveraging semantic matching techniques to derive user and ad representations. It extends beyond the traditional feature-CTR mapping by including the interactions and preferences embedded within user behaviors and ad attributes.
2. Correlation Subnet: By capitalizing on concepts from the skip-gram model, this subnet captures ad-ad relationships. It focuses on temporal associations among ads within the same user interaction session, thereby enriching ad embedding representations.
3. Prediction Subnet: A conventional deep neural network (DNN), this subnet models the feature-CTR relationship, integrating learned embeddings to predict the likelihood of clicks effectively.

The joint optimization of these subnets under supervised learning enables the extraction of robust feature representations with enhanced statistical reliability. This integration leads to a simultaneous improvement in representation learning and predictive accuracy.

Experimental Evaluation and Results

The experimentation conducted on two large-scale datasets, including a commercial dataset from Alibaba, provides substantial evidence for the effectiveness of the DeepMCP model. The model outperforms several contemporary baseline approaches such as Logistic Regression (LR), Factorization Machine (FM), Wide{content}Deep, and DeepFM in terms of AUC and log loss metrics. Key observations include:

• AUC Improvement: DeepMCP achieves higher AUC scores compared to traditional models, showcasing its ability to more accurately rank clicked ads over non-clicked ones.
• Log Loss Reduction: The inclusion of matching and correlation subnets contributes to a decrease in log loss, further validating the model's prediction accuracy.

The paper also explores the effects of varying balancing parameters, layer sizes, and depths on the model's performance, providing insights into the optimal configuration settings for DeepMCP.

Implications and Future Directions

The proposed DeepMCP model introduces promising advancements in CTR prediction by incorporating additional relational learning into model training. It offers a significant improvement in CTR prediction by considering relational embeddings that capture user interests and ad correlations. This dual focus not only mitigates data sparsity issues but also enhances prediction robustness and adaptability to dynamic user behaviors.

The implications of such methodologies suggest broader applicability in other domains requiring intricate multi-feature interaction modeling, such as recommendation systems and user behavior analysis. Future research may explore the integration of more complex relationships and leveraging transformer-based architectures to further enhance the modeling of contextual interactions in CTR prediction tasks. Additionally, harmonizing these strategies with existing real-time bidding systems could lead to real-world applications that continuously refine targeting accuracy and advertising profitability.

In conclusion, the DeepMCP model represents a stride towards sophisticated and nuanced CTR prediction techniques, providing a foundation for future exploration into more comprehensive multi-faceted learning applications within the advertising ecosystem.