Advertising keyword generation using active learning

ABSTRACT

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.

REFERENCES

• 1: Vibhanshu Abhishek , Kartik Hosanagar, Keyword generation for search engine advertising using semantic similarity between terms, Proceedings of the ninth international conference on Electronic commerce, August 19-22, 2007, Minneapolis, MN, USA [doi>10.1145/1282100.1282119]
• 2: Yifan Chen , Gui-Rong Xue , Yong Yu, Advertising keyword suggestion based on concept hierarchy, Proceedings of the international conference on Web search and web data mining, February 11-12, 2008, Palo Alto, California, USA [doi>10.1145/1341531.1341564]
• 3: Amruta Joshi , Rajeev Motwani, Keyword Generation for Search Engine Advertising, Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops, p.490-496, December 18-22, 2006 [doi>10.1109/ICDMW.2006.104]
• 4: Kai Yu , Jinbo Bi , Volker Tresp, Active learning via transductive experimental design, Proceedings of the 23rd international conference on Machine learning, p.1081-1088, June 25-29, 2006, Pittsburgh, Pennsylvania [doi>10.1145/1143844.1143980]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services

General Terms:
Algorithms, Design, Experimentation

Keywords:
active learning, keyword generation, sponsored search