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pro vyhledávání: '"Suming J. Chen"'
Publikováno v:
KDD
Many modern recommender systems train their models based on a large amount of implicit user feedback data. Due to the inherent bias in this data (e.g., position bias), learning from it directly can lead to suboptimal models. Recently, unbiased learni
Autor:
Brian Calaci, Zhen Qin, Ryan Evans, Sandeep Tata, Michael Rose, Sean Abraham, Zac Wilson, Suming J. Chen, Michael Colagrosso, Donald Metzler
Publikováno v:
KDD
Quick Access is a machine-learned system in Google Drive that predicts which files a user wants to open. Adding Quick Access recommendations to the Drive homepage cut the amount of time that users spend locating their files in half. Aggregated over t
Publikováno v:
WWW
Neural search ranking models have been not only actively studied in the information retrieval community, but also widely adopted in real-world industrial applications. However, due to the non-convexity and stochastic training of neural model formulat
Publikováno v:
WWW (Companion Volume)
Machine-learning (ML) models are ubiquitously used to make a variety of inferences, a common application being to predict and categorize user behavior. However, ML models often suffer from only being exposed to biased data – for instance, a search
Publikováno v:
WSDM
Retrieval effectiveness in information retrieval systems is heavily dependent on how various parameters are tuned. One option to find these parameters is to run multiple online experiments and using a parameter sweep approach in order to optimize the
Publikováno v:
Journal of Artificial Intelligence Research. 49:601-633
When making decisions under uncertainty, the optimal choices are often difficult to discern, especially if not enough information has been gathered. Two key questions in this regard relate to whether one should stop the information gathering process