Search Results for author:
Found 12 papers, 3 papers with code
Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences
SAM supports efficient training and real-time inference for user behavior sequences with lengths on the scale of thousands.
A General Traffic Shaping Protocol in E-Commerce
Our algorithm can straightforwardly optimize the linear programming in the prime space, and its solution can be simply applied by a stochastic strategy to fulfill the optimized objective and the constraints in expectation.
Iterative Memory Network for Long Sequential User Behavior Modeling in Recommender Systems
Extensive empirical studies show that our method outperforms various state-of-the-art sequential modeling methods on both public and industrial datasets for long sequential user behavior modeling.
Learning-To-Ensemble by Contextual Rank Aggregation in E-Commerce
Combined with the idea of Bayesian Optimization and gradient descent, we solve the online contextual Black-Box Optimization task that finds the optimal weights for sub-models given a chosen RA model.
Imitate TheWorld: A Search Engine Simulation Platform
Recent E-commerce applications benefit from the growth of deep learning techniques.
Diversity Regularized Interests Modeling for Recommender Systems
Each interest of the user should have a certain degree of distinction, thus we introduce three strategies as the diversity regularized separator to separate multiple user interest vectors.
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
In this paper, we propose a novel neural network framework ESDF to tackle the above three challenges simultaneously.
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator that ensures the generalization of the evaluator.
Policy Optimization with Model-based Explorations
Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games.
Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design
In a large E-commerce platform, all the participants compete for impressions under the allocation mechanism of the platform.
Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning
Applying reinforcement learning in physical-world tasks is extremely challenging.
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session.
