Sponsored search is an important monetization channel for search engines, in which machine learning techniques are used to select the ads shown to users from advertisers. In the past few years, dramatic advances have been witnessed in the field of machine learning techniques in sponsored search. In this presentation, we will first provide overview of machine learning methods and some commonly used open source machine learning tools. After that, we show some practical applications of sponsored search via machine learning techniques, such as, supervised and semi-supervised query classification, query intents identification, fine-grained query clustering. All results indicate that using machine learning techniques allow us to achieve good performance for different real-world sponsored search tasks. 

Qi Ye received his PhD degree in computer science at Beijing University of Posts and Telecommunications in 2011. He has been dedicated in the research of sponsored search since then. Today he serves as an expert researcher in ADRS (Advertisement Research for Sponsored Search) group for Sogou Inc. His research interests include query intent detection, short text classification, short text clustering, short text relevance, graph mining, and their applications in sponsored search. Most of the research results have been used in online deployed systems, and some of them have been published.