Joint Embedding of Query and Ad by Leveraging Implicit Feedback

Sponsored search is at the center of a multibil-lion dollar market established by search tech-nology. Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities. Lexical fea-tures derived from the text of both the query and ads play a significant role, complementing features based on historical click information. The purpose of this paper is to explore the use of word embedding techniques to generate ef-fective text features that can capture not only lexical similarity between query and ads but also the latent user intents. We identify several potential weaknesses of the plain application of conventional word embedding methodolo-gies for ad click prediction. These observa-tions motivated us to propose a set of novel joint word embedding methods by leveraging implicit click feedback. We verify the effec-tiveness of these new word embedding models by adding features derived from the new mod-els to the click prediction system of a com-mercial search engine. Our evaluation results clearly demonstrate the effectiveness of the proposed methods. To the best of our knowl-edge this work is the first successful applica-tion of word embedding techniques for the task of click prediction in sponsored search.