Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Abstract

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features.

References in corpus (2)

• XGBoost: A Scalable Tree Boosting System
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

Cited by in corpus (12)

• Click-Through Rate Prediction in Online Advertising: A Literature Review
• An Embedding Learning Framework for Numerical Features in CTR Prediction
• Graph Convolution Machine for Context-aware Recommender System
• CL4CTR: A Contrastive Learning Framework for CTR Prediction
• Click-Through Rate Prediction with Multi-Modal Hypergraphs
• Enhancing CTR Prediction with Context-Aware Feature Representation Learning
• Embedding Compression in Recommender Systems: A Survey
• Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
• A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction
• Retrieval-Oriented Knowledge for Click-Through Rate Prediction
• AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query
• Dual-Stream MLP is All You Need for CTR Prediction