An adaptive hybrid XdeepFM based deep Interest network model for click-through rate prediction system

Abstract

Recent advances in communication enable individuals to use phones and computers to access information on the web. E-commerce has seen rapid development, e.g., Alibaba has nearly 12 hundred million customers in China. Click-Through Rate (CTR) forecasting is a primary task in the e-commerce advertisement system. From the traditional Logistic Regression algorithm to the latest popular deep neural network methods that follow a similar embedding and MLP, several algorithms are used to predict CTR. This research proposes a hybrid model combining the Deep Interest Network (DIN) and eXtreme Deep Factorization Machine (xDeepFM) to perform CTR prediction robustly. The cores of DIN and xDeepFM are attention and feature cross, respectively. DIN follows an adaptive local activation unit that incorporates the attention mechanism to adaptively learn user interest from historical behaviors related to specific advertisements. xDeepFM further includes a critical part, a Compressed Interactions Network (CIN), aiming to generate feature interactions at a vectorwise level implicitly. Furthermore, a CIN, plain DNN, and a linear part are combined into one unified model to form xDeepFM. The proposed end-to-end hybrid model is a parallel ensemble of models via multilayer perceptron. CIN and xDeepFM are trained in parallel, and their output is fed into a multilayer perceptron. We used the e-commerce Alibaba dataset with the focal loss as the loss function for experimental evaluation through online complex example mining (OHEM) in the training process. The experimental result indicates that the proposed hybrid model has better performance than other models.

Usability and Security Testing of Online Links: A Framework for Click-Through Rate Prediction Using Deep Learning

“…Lu et al [43] present a hybrid model for CTR prediction that combines the deep interest network (DIN) with the eXtreme Deep FM (xDeepFM). DIN employs an adaptive local activation unit that integrates an attention unit to learn user interest from past actions associated with certain adverts.…”

Targeted Advertising in Social Media Platforms Using Hybrid Convolutional Learning Method besides Efficient Feature Weights

“…Taobao is an advertising dataset provided by Alibaba, containing eight days of ad clickthrough data (26 million records) randomly sampled from 1,140,000 users (Table 1). Following the original data split, the first seven days (i.e., 20170506-20170512) of samples are used for training, while the last day's samples (20170513) are used for testing [55].…”

Disentangled self-attention neural network based on information sharing for click-through rate prediction

“…Deep learning becomes a valuable model for evaluating online user reaction rate issues like advertising click-through rates (CTR) based on the learning mentioned above capability. CTR prediction plays a vital role in industrial online advertising and recommendation systems ( Graepel et al, 2010 ; Lu, 2021 ), and its purpose is to determine whether to recommend the item to users based on the likelihood of users clicking on the item. In CTR prediction, feature interaction is a common technique that combines different features to form new features to capture the interaction and nonlinear relationships between features.…”