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Collaborative filtering (CF) has been widely employed within recommender systems to solve many real-world problems. Learning effective latent factors plays the most important role in collaborative filtering. Traditional CF methods based upon matrix factorization techniques learn the latent factors from the user-item ratings and suffer from the cold start(More)
A widely used method for estimating counterfactu-als and causal treatment effects from observational data is nearest-neighbor matching. This typically involves pairing each treated unit with its nearest-in-covariates control unit, and then estimating an average treatment effect from the set of matched pairs. Although straightforward to implement, this(More)
Massively Multiplayer Online Role Playing Games(MMORPGs) are computer based games in which players interactwith one another in the virtual world. Worldwide revenuesfor MMORPGs have seen amazing growth in last few years and itis more than a 2 billion dollars industry as per current estimates.Huge amount of revenue potential has attracted several(More)
Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal trade-off between exploration and exploitation (otherwise known as the bandit problem), a crucial problem in collaborative filtering from cold-start, has not been previously addressed. In this paper,(More)
Pressure dipoles in global climate data capture recurring and persistent, large-scale patterns of pressure and circulation anomalies that span distant geographical areas (teleconnec-tions). In this paper, we present a novel graph based approach called shared reciprocal nearest neighbors that considers only reciprocal positive and negative edges in the(More)
Dipoles represent long distance connections between the pressure anomalies of two distant regions that are negatively correlated with each other. Such dipoles have proven important for understanding and explaining the variability in climate in many regions of the world, e.g., the El Nino climate phenomenon is known to be responsible for precipitation and(More)
Earth science data consists of a strong seasonality component as indicated by the cycles of repeated patterns in climate variables such as air pressure, temperature and precipitation. The seasonality forms the strongest signals in this data and in order to find other patterns, the seasonality is removed by subtracting the monthly mean values of the raw data(More)