Improving Product Review Search Experiences in General Search Engines.

Proceedings of the International Conference on Electronic Commerce – EC2009. 2009
Improving Product Review Search Experiences in General Search Engines.
Shen Huang, Dan Shen, Wei Feng, Catherine Baudin, Yongzheng Zhang, Shen Huang, Dan Shen, Wei Feng, Catherine Baudin, Yongzheng Zhang

In the Web 2.0 era, internet users contribute a large amount of online content. Product review is a good example. Since these phenomena are distributed all over shopping sites, weblogs, forums etc., most people have to rely on general search engines to discover and digest others' comments. While conventional search engines work well in many situations, it's not sufficient for users to gather such information.

The reasons include but are not limited to: 1) the ranking strategy does not incorporate product reviews' inherent characteristics, e.g., sentiment orientation; 2) the snippets are neither indicative nor descriptive of user opinions. In this paper, we propose a feasible solution to enhance the experience of product review search.

Based on this approach, a system named "Improved Product Review Search (IPRS)" is implemented on the ground of a general search engine. Given a query on a product, our system is capable of: 1) automatically identifying user opinion segments in a whole article; 2) ranking opinions by incorporating both the sentiment orientation and the topics expressed in reviews; 3) generating readable review snippets to indicate user sentiment orientations; 4) easily comparing products based on a visualization of opinions.

Both results of a usability study and an automatic evaluation show that our system is able to assist users quickly understand the product reviews within limited time.

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.