Data Design for Personalization: Current Challenges and Emerging Opportunities

Workshop at WSDM-2014
Data Design for Personalization: Current Challenges and Emerging Opportunities
Elizabeth Churchill, Atish Das Sarma
Abstract

Personalization is central to most Internet experiences. Personalization is a data-driven process, whether the data are explicitly gathered (e.g., by asking people to fill out forms) or implicitly (e.g. through analysis of behavioral data).

It is clear that designing for effective personalization poses interesting engineering and computer science challenges. However, personalization is also a user experience issue. We believe that encouraging dialogue and collaboration between data mining experts, content providers, and user-focused researchers will offer gains in the area of personalization for search and for other domains.

This workshop is part of a larger effort we are developing: D2D: Data to Design - Design to Data.

Our vision is to provide a forum for researchers and practitioners in computer and systems sciences, data sciences, machine learning, information retrieval, interaction and interface design, and human computer interaction to interact.

Our goal is to explore issues surrounding content and presentation personalization across different devices, and to set an agenda for cross-discipline, collaborative engagement.

Another publication from the same category: Machine Learning and Data Science

IEEE Computing Conference 2018, London, UK

Regularization of the Kernel Matrix via Covariance Matrix Shrinkage Estimation

The kernel trick concept, formulated as an inner product in a feature space, facilitates powerful extensions to many well-known algorithms. While the kernel matrix involves inner products in the feature space, the sample covariance matrix of the data requires outer products. Therefore, their spectral properties are tightly connected. This allows us to examine the kernel matrix through the sample covariance matrix in the feature space and vice versa. The use of kernels often involves a large number of features, compared to the number of observations. In this scenario, the sample covariance matrix is not well-conditioned nor is it necessarily invertible, mandating a solution to the problem of estimating high-dimensional covariance matrices under small sample size conditions. We tackle this problem through the use of a shrinkage estimator that offers a compromise between the sample covariance matrix and a well-conditioned matrix (also known as the "target") with the aim of minimizing the mean-squared error (MSE). We propose a distribution-free kernel matrix regularization approach that is tuned directly from the kernel matrix, avoiding the need to address the feature space explicitly. Numerical simulations demonstrate that the proposed regularization is effective in classification tasks.

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