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Deep Interest Network for Click-Through Rate Prediction (2017)
In this paper, we introduce a new proposed model, Deep Interest Network (DIN), which represents users’ diverse interests with an interest distribution and designs an attention-like network structure to locally activate the related interests according to the candidate ad, which is proven to be effective and significantly outperforms traditional model.
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017)
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.
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Wide & Deep Learning for Recommender Systems (2016)
In this paper, we present Wide & Deep learning — jointly trained wide linear models and deep neural networks — to combine the benefits of memorization and generalization for recommender systems.
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Factorization Machines (2010)
In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models.
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Practical Lessons from Predicting Clicks on Ads at Facebook (2014)
In this paper we introduce a model which combines decision trees with logistic regression, outperforming either of these methods on its own by over 3%, an improvement with significant impact to the overall system performance.
We then explore how a number of fundamental parameters impact the final prediction performance of our system.
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