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Behavioral targeting (BT) is a valuable tool for online advertising. In this paper, we study a new problem of incorporating social information into traditional behavior targeting models. Specifically, we present a social regularization based Poisson regression framework for behavior targeting. Based on the observation that social information can be diverse and competing, we furthermore present two...
Related data streams refer to data streams that can be joined together by matching their join attributes. Existing research on learning from related data streams is based on an assumption that all streams arrive at a central processing unit in a synchronous way, such that in an arbitrary sliding window, all tuples of the streams can be perfectly joined together. This assumption, however, does not...
In this paper, we propose a framework to build prediction models from data streams which contain both labeled and unlabeled examples. We argue that due to the increasing data collection ability but limited resources for labeling, stream data collected at hand may only have a small number of labeled examples, whereas a large portion of data remain unlabeled but can be beneficial for learning. Unleashing...
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