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Behavioral targeting (BT) leverages historical user behavior to select the ads most relevant to users to display. The state-of-the-art of BT derives a linear Poisson regression model from fine-grained user behavioral data and predicts click-through rate (CTR) from user history. We designed and...
Given a spatial dataset placed on an n × n grid, our goal is to find the rectangular regions within which subsets of the dataset exhibit anomalous behavior. We develop algorithms that, given any user-supplied arbitrary likelihood function, conduct a likelihood ratio hypothesis test (LRT) over...
A fundamental challenge in utilizing Web search click data is to infer user-perceived relevance from the search log. Not only is the inference a difficult problem involving statistical reasonings but the bulky size, together with the ever-increasing nature, of the log data imposes extra...
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