Rare Query Expansion Through Generative Adversarial Networks in Search Advertising
published: Nov. 23, 2018, recorded: August 2018, views: 677
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !