A Novel Integrated Network with LightGBM for Click-Through Rate Prediction

Abstract

Click-through Rate (CTR) prediction has become one of the core tasks of the recommendation system and its online advertising with the development of e-commerce. In the CTR prediction field, different features extraction schemes are used to mine the user click behavior to achieve the maximum CTR, which helps the advertisers maximize their profits. At present, achievements have been made in CTR prediction based on Deep Neural Network (DNN), but insufficiently, DNN can only learn high-order features combination. In this paper, Product & Cross supported Stacking Network with LightGBM (PCSNL) is proposed for CTR prediction to solve such problems. Firstly, the L 1 and L 2 regularizations are imposed on Light Gradient Boosting Machine (LightGBM) to prevent overfitting. Secondly, the method of vector-wise feature interactions is added to product layer in product network to learn second-order feature combinations. Lastly, feature information is fully learned through the cross network, product network and stacking network in PCSNL. The online ads CTR prediction datasets released by Huawei and Avazu on the Kaggle platform are involved for experiments. It is shown that the PCSN model and PCSNL have better performance than the traditional CTR prediction models and deep learning models.

It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation

“…In the last years, CTR-P became a core task in online advertisement (also called ads) [7,8]. This is mainly because search engines, and especially recommender systems, are playing a significant role in e-commerce businesses [9][10][11][12].…”