Search Results for author:
Found 11 papers, 3 papers with code
Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
Finally, we employ a novel mutual unit to adaptively learn the similarity between various scenarios and incorporate it into multi-branch network.
Cultural Vocal Bursts Intensity Prediction
Recommendation Systems
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
In this paper, we present a novel Generator and Critic slate re-ranking approach, where the Critic evaluates the slate and the Generator ranks the items by the reinforcement learning approach.
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.
Matrix embedding method in match for session-based recommendation
But this method repudiate the variousness of the interest of user in a session.
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.
Selective Zero-Shot Classification with Augmented Attributes
We propose a selective zero-shot classifier based on both the human defined and the automatically discovered residual attributes.
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.
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.
Dual Swap Disentangling
To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs.
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.
