Query Suggestion with Large Scale Data

Chapter 20: Volume 31: Handbook of Statistics, 1st Edition : Machine Learning : Theory and Applications
Query Suggestion with Large Scale Data
Nish Parikh, Gyanit Singh, Neel Sundaresan
eBay Authors

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Another publication from the same author:

In proceedings of the Workshop on Log-based Personalization (the 4th WSCD workshop) at WSDM 2014

A Large Scale Query Logs Analysis for Assessing Personalization Opportunities in E-commerce Sites

Neel Sundaresan, Zitao Liu

Personalization offers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics

(1) What user information is useful for personalization;

(2) Importance of per-query personalization

(3) Importance of recency in query prediction.

In this paper, we study these problems and provide some preliminary conclusions


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.