Generative Adversarial Networks (GAN) have achieved great success in generating realistic synthetic data like images, tags, and sentences. We explore using GAN to generate bid keywords directly from query in sponsored search ads selection, especially for rare queries. Specifically, in the query expansion (query-keyword matching) scenario in search advertising, we train a sequence to sequence model as the generator to generate keywords, conditioned on the user query, and use a recurrent neural network model as the discriminator to play an adversarial game with the generator. By applying the trained generator, we can generate keywords directly from a given query, so that we can highly improve the effectiveness and efficiency of query-keyword matching based ads selection in search advertising. We trained the proposed model in the clicked query-keyword pair dataset from a commercial search advertising system. Evaluation results show that the generated keywords are more relevant to the given query compared with the baseline model and they have big potential to bring extra revenue improvement.

Dual coordinate descent method is one of the most effective approaches for large-scale linear classification. However, its sequential design makes the parallelization difficult. In this work, we target at the parallelization in a multi-core environment. After pointing out difficulties faced in some existing approaches, we propose a new framework to parallelize the dual coordinate descent method. The key idea is to make the majority of all operations (gradient calculation here) parallelizable. The proposed framework is shown to be theoretically sound. Further, we demonstrate through experiments that the new framework is robust and efficient in a multi-core environment.

Shared-memory systems such as regular desktops now possess enough memory to store large data. However, the training process for data classification can still be slow if we do not fully utilize the power of multi-core CPUs. Many existing works proposed parallel machine learning algorithms by modifying serial ones, but convergence analysis may be complicated. Instead, we do not modify machine learning algorithms, but consider those that can take the advantage of parallel matrix operations. We particularly investigate the use of parallel sparse matrix-vector multiplications in a Newton method for large scale logistic regression. Various implementations from easy to sophisticated ones are analyzed and compared. Results indicate that under suitable settings excellent speedup can be achieved.