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Found 8 papers, 4 papers with code
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging
To alleviate this issue, we propose to extract knowledge from the \textit{super-domain} that contains web-scale and long-time impression data, and further assist the online recommendation task (downstream task).
Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
We further show that such a process is equivalent to adding an adversarial perturbation to the model input, and thereby name our proposed approach as an the Adversarial Gradient Driven Exploration (AGE).
CAN: Feature Co-Action for Click-Through Rate Prediction
For example, a simple attempt to learn the combination of feature A and feature B <A, B> as the explicit cartesian product representation of new features can outperform previous implicit feature interaction models including factorization machine (FM)-based models and their variations.
Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling
In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model.
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
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.
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
Deep Interest Evolution Network for Click-Through Rate Prediction
Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt
Ranked #1 on
Click-Through Rate Prediction
on Amazon Dataset
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time.
