Dmitry Pavlov

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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 implemented a highly scalable and efficient solution to BT using(More)
We investigate the problem of generating fast approximate answers to queries posed to large sparse binary data sets. We focus in particular on probabilistic model-based approaches to this problem and develop a number of techniques that are significantly more accurate than a baseline independence model. In particular, we introduce two techniques for building(More)
Numerical viscosity has long been a problem in fluid animation. Existing methods suffer from intrinsic artificial dissipation and often apply complicated computational mechanisms to combat such effects. Consequently, dissipative behavior cannot be controlled or modeled explicitly in a manner independent of time step size, complicating the use of coarse(More)
Support vector machines (SVMs) provide classi cation models with strong theoretical foundations as well as excellent empirical performance on a variety of applications. One of the major drawbacks of SVMs is the necessity to solve a large-scale quadratic programming problem. This paper combines likelihood-based squashing with a probabilistic formulation of(More)
In vitro effects of cadmium (0.5-50 mg/l) and DDVP (0.2-100 mg/l) on the total amylolytic, sucrase and protease activities of intestinal mucosa have been studied for the first time in 11 freshwater teleosts. Total amylolytic activity in burbot, crucian carp and common carp, sucrase activity in blue bream and total proteolytic activity in burbot and pike(More)
Enterprise and web data processing and content aggregation systems often require extensive use of human-reviewed data (e.g. for training and monitoring machine learning-based applications). Today these needs are often met by in-house efforts or out-sourced offshore contracting. Emerging applications attempt to provide automated collection of human-reviewed(More)
We develop a maximum entropy (maxent) approach to generating recommendations in the context of a user’s current navigation stream, suitable for environments where data is sparse, high-dimensional, and dynamic— conditions typical of many recommendation applications. We address sparsity and dimensionality reduction by first clustering items based on user(More)
We present a mixture model based approach for learning individualized behavior models for the Web users. We investigate the use of maximum entropy and Markov mixture models for generating probabilistic behavior models. We first build a global behavior model for the entire population and then personalize this global model for the existing users by assigning(More)
Naive Bayes classifier has long been used for text categorization tasks. Its sibling from the unsupervised world, the probabilistic mixture of multinomial models, has likewise been successfully applied to text clustering problems. Despite the strong independence assumptions that these models make, their attractiveness come from low computational cost,(More)