3 papers
cs.IR2019
Neural Document Expansion with User Feedback
Yue Yin, Chenyan Xiong, Cheng Luo +1
This paper presents a neural document expansion approach (NeuDEF) that enriches document representations for neural ranking models. NeuDEF harvests expansion terms from queries whi…
cs.CE2018
Temporal Relational Ranking for Stock Prediction
Fuli Feng, Xiangnan He, Xiang Wang +3
Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on…
cs.IR2018
Unbiased Learning to Rank with Unbiased Propensity Estimation
Qingyao Ai, Keping Bi, Cheng Luo +2
Learning to rank with biased click data is a well-known challenge. A variety of methods has been explored to debias click data for learning to rank such as click models, result int…