Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.
( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )
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These subnets model the user-ad, ad-ad and feature-CTR relationship respectively.
To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands.
Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services.
This paper studies graph-based recommendation, where an interaction graph is constructed built from historical records and is lever-aged to alleviate data sparsity and cold start problems.
The key of this task is to model feature interactions among different feature fields.
With the rapid development of online advertising and recommendation systems, click-through rate prediction is expected to play an increasingly important role. Recently many DNN-based models which follow a similar Embedding&MLP paradigm have been proposed, and have achieved good result in image/voice and nlp fields.
Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.