DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (1703.04247v1)

Abstract: Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.

• The paper introduces DeepFM, a novel model that merges FM and DNN to learn both low- and high-order feature interactions.
• It eliminates the need for pre-training and manual feature engineering by learning directly from raw data.
• Empirical results demonstrate that DeepFM outperforms state-of-the-art models on AUC and Logloss metrics, boosting efficiency and accuracy.

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

The paper "DeepFM: A Factorization-Machine based Neural Network for CTR Prediction" introduces an advanced approach for improving click-through rate (CTR) predictions in recommender systems via a novel neural network architecture termed DeepFM. This model addresses several key limitations in existing techniques by integrating the strengths of both factorization machines (FM) and deep neural networks (DNN) to capture feature interactions from raw data without the need for meticulous feature engineering.

Introduction

CTR prediction is essential in recommendation systems for optimizing the probability that users will click on recommended items. Existing methods often emphasize either low-order or high-order feature interactions and typically require extensive feature engineering. The DeepFM model proposed in this paper pioneers an end-to-end learning mechanism that captures feature interactions of all orders by seamlessly combining FM and DNN, mitigating the necessity for manual feature engineering.

Architecture of DeepFM

DeepFM's architecture consists of two primary components:

1. FM Component: This part captures low-order feature interactions using the inner product of latent feature vectors. It's effective at modeling pairwise interactions especially in sparse datasets, and it does so efficiently by representing interactions via low-dimensional latent vectors.
2. Deep Component: This segment is a feed-forward neural network aimed at capturing high-order feature interactions. It uses embeddings derived from raw categorical and continuous features to abstract rich, high-level interactions crucial for making accurate CTR predictions.

Notably, DeepFM shares the same feature embeddings between its FM and DNN components. This shared input simplifies the architecture and reduces the training complexity, while ensuring that both low- and high-order interactions are learned concurrently and effectively.

Comparison with Existing Models

DeepFM is compared against several state-of-the-art models including:

• FNN: A neural network initialized with pretrained factorization machine embeddings.
• PNN: Employs a product layer to capture high-order interactions but is computationally intensive.
• Wide & Deep Models: These Google-proposed hybrid models combine linear and deep components but require substantial feature engineering.

In contrast to these models, DeepFM offers several distinct advantages. It requires no pre-training and eliminates the need for crafting feature interactions manually, learning directly from raw data. This is a significant edge over models like Wide & Deep, which demand bespoke feature engineering.

Empirical Evaluation

The paper reports extensive experiments on two datasets: the public Criteo dataset and a large-scale commercial dataset from a game center in an app store. The evaluation metrics employed are AUC (Area Under Curve) and Logloss, which are standard in CTR prediction tasks.

Key findings include:

• Efficiency: DeepFM demonstrates training efficiency comparable to the most optimized models in the literature. It is significantly faster than models requiring pre-training (e.g., FNN) or complex computations (e.g., PNN).
• Effectiveness: DeepFM consistently outperforms all baseline models including LR, FM, FNN, PNN, and Wide & Deep models. It achieves noteworthy improvements in AUC and Logloss scores, indicating superior predictive performance.

Future Directions and Implications

The implications of DeepFM's robust performance are far-reaching. By eliminating the need for feature engineering, DeepFM simplifies the model-building process, making advanced CTR prediction more accessible and scalable. It can be seamlessly integrated into existing systems with minimal adjustments, thereby enhancing the efficiency and accuracy of recommender systems across various domains.

The paper suggests two promising paths for future research: introducing pooling layers to enhance high-order interaction learning, and leveraging GPU clusters to handle large-scale datasets more effectively. These directions could further enhance DeepFM's applicability and robustness in real-world scenarios.

In conclusion, DeepFM represents a substantial advancement in CTR prediction models by successfully balancing simplicity, efficiency, and effectiveness, thus setting a new standard for future developments in recommender system research. This work holds potential for significant practical and theoretical contributions, propelling the field of recommender systems forward.