Advertising keyword generation using active learning

This paper proposes an efficient relevance feedback based interactive model for keyword generation in sponsored search advertising. We formulate the ranking of relevant terms as a supervised learning problem and suggest new terms for the seed by leveraging user relevance feedback information. Active learning is employed to select the most informative samples from a set of candidate terms for user labeling. Experiments show our approach improves the relevance of generated terms significantly with little user effort required. Categories and Subject Descriptors: H.3.5 [Information Systems]: Information Storage and Retrieval--On-line Information Services General Terms: Algorithms, Design, Experimentation

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Hao Wu, Guang Qiu, Xiaofei He, Yuan Shi, Mingcheng Qu, Jing Shen, Jiajun Bu, Chun Chen
• Keywords: Efficient Relevance Feedback, Internet Technology, Services General Terms, User Relevance Feedback, WWW 2009