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Motivated by applications in recommendation systems and bioinformatics, we consider the problem of completing a low rank, partially observed binary matrix with graph information. We show that the corresponding problem can be set up in a positive and unlabeled data learning (referred to as PU learning in literature) framework. We make connections to convex optimization and show that existing greedy...
We compute the Weiss-Weinstein bound in the context of change-point estimation in a multivariate time series whatever the considered distribution of the data as well the prior. Closed-form expressions are then given in the case of Gaussian observations with change of mean and variance and in the case of parameter change in a Poisson distribution. The proposed bound is shown to be tighter than the...
Design thinking framework is a powerful framework for innovation and design. It involves design specific cognitive activities such as Empathize, Define, Ideate, Prototype and Test (EDIPT). In this paper we present an implementation of EDIPT framework by novice educational technology (ET) researchers and investigate if the quality of research problems generated by the novice researchers is comparable...
Recommender systems (RS) have been popular for decades and many novel types of RS have been proposed and developed, such as context-aware recommender systems (CARS) which additionally take contexts (e.g., time, location, occasion, etc) into consideration to further assist users' decision makings. Meantime, the emergence of CARS also brings new recommendation opportunities, such as context suggestion...
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