111 citations · 111 across the 1 of their papers we have counts for
3 papers
cs.AI2018
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
Jun Feng, Heng Li, Minlie Huang +4
Ranking is a fundamental and widely studied problem in scenarios such as search, advertising, and recommendation. However, joint optimization for multi-scenario ranking, which aims…
stat.ML2018
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
Yabo Ni, Dan Ou, Shichen Liu +4
Tasks such as search and recommendation have become increas- ingly important for E-commerce to deal with the information over- load problem. To meet the diverse needs of di erent u…
stat.ML2017★ 111 cited
Cascade Ranking for Operational E-commerce Search
Shichen Liu, Fei Xiao, Wenwu Ou +1
In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at…