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.
['Hao Wu', 'Guang Qiu', 'Xiaofei He', 'Yuan Shi', 'Mingcheng Qu', 'Jing Shen', 'Jiajun Bu', 'Chun Chen']
Advertising keyword generation using active learning