Browsing by Subject "Side information"
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Item Efficient approaches in network inference(2016-12) Ray, Avik; Sanghavi, Sujay Rajendra, 1979-; Shakkottai, Sanjay; Baccelli, Francois; de Veciana, Gustavo; Caramanis, Constantine; Ravikumar, PradeepNetwork based inference is almost ubiquitous in modern machine learning applications. In this dissertation we investigate several such problems motivated by applications in social networks, biological networks, recommendation system, targeted advertising etc. Unavailability of the graph, presence of latent factors, and large network size often make these inference tasks challenging. We develop both generative models and efficient algorithms to solve such problems. We provide analytical guarantees, in terms of accuracy and computation time, for all our algorithms and demonstrate their applicability on many real datasets. This dissertation mainly consists of two parts. In the first part we consider three different problems. We first consider the task of learning the Markov network structure in a discreet graphical model. We develop three fast greedy algorithms to solve this problem which succeeds even in graphs with strong non-neighbor interaction where previous convex optimization based methods fail. Next we consider the problem of learning latent user interests in different topics, using cascades which spread over a network. Our new algorithm infers both user interests and topics in large cascades, better than standard topic modeling algorithms which do not consider the network structure. In the third problem we develop a novel recursive algorithm based on convex relaxation to detect overlapping communities in a graph. The second part of the dissertation develops a mathematical framework to handle different sources of side information and use it to improve inference in networks. However first we demonstrate a much general technique to incorporate variety of side information in estimating a single component of a mixture model e.g. Gaussian mixture model, latent Dirichlet allocation, subspace clustering, and mixed linear regression. We then use a similar technique to solve the problem of identifying a single target community in a graph, using reference nodes or biased node weights as side information. Our algorithms are based on a variant of method of moments, and are much faster and more accurate than other unsupervised and semi-supervised algorithms.Item A survey on using side information in recommendation systems(2012-05) Gunasekar, Suriya; Ghosh, Joydeep; Sanghavi, SujayThis report presents a survey of the state-of-the-art methods for building recommendation systems. The report mainly concentrates on systems that use the available side information in addition to a fraction of known affinity values such as ratings. Such data is referred to as Dyadic Data with Covariates (DyadC). The sources of side information being considered includes user/item entity attributes, temporal information and social network attributes. Further, two new models for recommendation systems that make use of the available side information within the collaborative filtering (CF) framework, are proposed. Review Quality Aware Collaborative Filtering, uses external side information, especially review text to evaluate the quality of available ratings. These quality scores are then incorporated into probabilistic matrix factorization (PMF) to develop a weighted PMF model for recommendation. The second model, Mixed Membership Bayesian Affinity Estimation (MMBAE), is based on the paradigm of Simultaneous Decomposition and Prediction (SDaP). This model simultaneously learns mixed membership cluster assignments for users and items along with a predictive model for rating prediction within each co-cluster. Experimental evaluation on benchmark datasets are provided for these two models.