Field-Embedded Factorization Machines for Click-through rate prediction (2009.09931v2)

Abstract: Click-through rate (CTR) prediction models are common in many online applications such as digital advertising and recommender systems. Field-Aware Factorization Machine (FFM) and Field-weighted Factorization Machine (FwFM) are state-of-the-art among the shallow models for CTR prediction. Recently, many deep learning-based models have also been proposed. Among deeper models, DeepFM, xDeepFM, AutoInt+, and FiBiNet are state-of-the-art models. The deeper models combine a core architectural component, which learns explicit feature interactions, with a deep neural network (DNN) component. We propose a novel shallow Field-Embedded Factorization Machine (FEFM) and its deep counterpart Deep Field-Embedded Factorization Machine (DeepFEFM). FEFM learns symmetric matrix embeddings for each field pair along with the usual single vector embeddings for each feature. FEFM has significantly lower model complexity than FFM and roughly the same complexity as FwFM. FEFM also has insightful mathematical properties about important fields and field interactions. DeepFEFM combines the FEFM interaction vectors learned by the FEFM component with a DNN and is thus able to learn higher order interactions. We conducted comprehensive experiments over a wide range of hyperparameters on two large publicly available real-world datasets. When comparing test AUC and log loss, the results show that FEFM and DeepFEFM outperform the existing state-of-the-art shallow and deep models for CTR prediction tasks. We have made the code of FEFM and DeepFEFM available in the DeepCTR library (https://github.com/shenweichen/DeepCTR).

• The paper introduces FEFM and DeepFEFM for effective CTR prediction by capturing field-specific feature interactions.
• The models use symmetric matrix embeddings to lower complexity and improve interpretability compared to traditional FFM.
• Experimental results on Avazu and Criteo datasets show enhanced AUC and lower log loss, outperforming other shallow and deep models.

Overview of "Field-Embedded Factorization Machines for Click-Through Rate Prediction"

The paper "Field-Embedded Factorization Machines for Click-Through Rate Prediction" introduces two models for enhancing click-through rate (CTR) prediction: the Field-Embedded Factorization Machine (FEFM) and its deep learning counterpart, Deep Field-Embedded Factorization Machine (DeepFEFM). These models focus on capturing field-specific feature interactions, which are crucial for improving CTR prediction in digital advertising and recommender systems.

Key Contributions and Methodology

FEFM Model: The FEFM model innovates upon existing factorization machine variants by introducing symmetric matrix embeddings for each field pair. This approach allows the model to effectively capture field-specific interactions without incurring the high complexity often associated with the Field-Aware Factorization Machine (FFM). The authors demonstrate that FEFM has a significantly lower model complexity compared to FFM and performs competitively with other shallow models like the Field-weighted Factorization Machine (FwFM).

DeepFEFM Model: Building on FEFM, the DeepFEFM architecture integrates a deep neural network (DNN) layer to capture higher-order interactions. By combining FEFM-generated interaction vectors with feature embeddings, DeepFEFM is designed to exploit the benefits of shallow and deep models, providing a robust solution for CTR prediction tasks.

Mathematical Properties: A crucial aspect of FEFM is its ability to provide interpretable field interactions. The eigenvalues of the matrix embeddings are used to quantify interaction strength between fields, revealing insights into which interactions are most important in the dataset.

Experimental Results

The authors conducted extensive experiments using publicly available datasets, including Avazu and Criteo, to benchmark FEFM and DeepFEFM against state-of-the-art models. Key findings include:

• Performance: FEFM and DeepFEFM consistently outperform existing shallow and deep models respectively, in terms of both test AUC and log loss.
• Efficiency: FEFM demonstrates a lower parameter count compared to FFM while achieving superior predictive performance.
• Interpretability: The symmetry in FEFM matrix embeddings aids in interpreting the interaction strengths between field pairs, which aligns with intuitive domain knowledge.

Implications and Future Directions

Practically, the proposed models offer improved CTR prediction by focusing on meaningful feature interactions, which enhances both user experience and advertising revenue. Theoretically, the introduction of symmetric matrix embeddings provides a new avenue for embedding interactions in machine learning models, potentially extending to other domains where field-specific insights are valuable.

In terms of future developments, expanding these models to incorporate real-time adaptations or exploring further reductions in model complexity while maintaining accuracy could be promising directions. Additionally, investigating the application of these models in other domains such as content recommendation or personalized marketing could exploit their strengths in handling large-scale, multifaceted datasets.

Conclusion

The paper presents a significant advancement in CTR prediction through the introduction of FEFM and DeepFEFM. By addressing both model complexity and interpretability, the authors provide a valuable contribution that balances the need for practicality in deployment with high predictive accuracy. This work sets the stage for further exploration into field-specific modeling techniques and their applications across various machine learning tasks.