262 citations · 1k across the 32 of their papers we have counts for
7 papers · 1 filter
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…
Multi-Source Pointer Network for Product Title Summarization
Fei Sun, Peng Jiang, Hanxiao Sun +3
In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices. Comparing with conventional sentence summarization, produc…
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…
Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
Tao Zhuang, Wenwu Ou, Zhirong Wang
In web search, mutual influences between documents have been studied from the perspective of search result diversification. But the methods in web search is not directly applicable…
Automatic Generation of Chinese Short Product Titles for Mobile Display
Yu Gong, Xusheng Luo, Kenny Q. Zhu +3
This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices. It is a ne…
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Yu Gong, Xusheng Luo, Yu Zhu +6
Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such model…
Alternating Multi-bit Quantization for Recurrent Neural Networks
Chen Xu, Jianqiang Yao, Zhouchen Lin +4
Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. F…