Recent years have witnessed dramatic changes in how people interact with search engines. Search engines are expected to be more intelligent in understanding users' intention and fulfilling users' needs with direct answers rather than raw information. Furthermore, search engines are expected to be equipped with recommendation and dialogue capabilities, making the interaction with users more natural and smoother. In this talk, I will introduce Baidu's work on how to make some of them come true through the deep understanding of users, queries and web pages, and discuss challenges behind these technologies.

The amount of online photos and videos is now at the scale of tens of billions. To organize, index, and retrieve these large-scale rich-media data, a system must employ scalable data management and mining algorithms. The research community needs to consider solving large scale problems rather than solving problems with small datasets that do not reflect real life scenarios. This tutorial introduces key challenges in large-scale rich-media data mining, and presents parallel algorithms for tackling such challenges. We present our parallel implementations of Spectral Clustering (PSC), FP-Growth (PFP), Latent Dirichlet Allocation (PLDA), and Support Vector Machines (PSVM).

The kernel methods play a pivotal role in machine learning algorithms. Unfortunately, working with the kernel methods must deal with an n times n kernel matrix, which is memory intensive. In this paper, we present a parallel, approximate matrix factorization algorithm, which loads only essential data to individual processors to enable parallel processing of data. Our method reduces space requirement for the kernel matrix from O(n2) to O(np/m), where n is the amount of data, p the reduced matrix dimension (p << n), and m the number of processors.

Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation. Let $n$ denote the number of training instances, $p$ the reduced matrix dimension after factorization ($p$ is significantly smaller than $n$), and $m$ the number of machines. PSVM reduces the memory requirement from $\MO$($n^2$) to $\MO$($np/m$), and improves computation time to $\MO$($np^2/m$). Empirical studies on up to $500$ computers shows PSVM to be effective.