1 citations · 1 across the 6 of their papers we have counts for
6 papers
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed
Xiaowen Shi, Ze Wang, Yuanying Cai +6
Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and the…
PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
Xiaowen Shi, Fan Yang, Ze Wang +6
Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands.…
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation
Ze Wang, Guogang Liao, Xiaowen Shi +6
Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot. Notice that…
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks
Ze Wang, Guogang Liao, Xiaowen Shi +5
With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and…
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation
Guogang Liao, Xiaowen Shi, Ze Wang +5
A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, modeling user…
A Real-Time Deep Network for Crowd Counting
Xiaowen Shi, Xin Li, Caili Wu +3
Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising…