Exploiting Contextual Factors for Click Modeling in Sponsored Search

Dawei Yin, Shike Mei, Bin Cao, Jian-Tao Sun and Brian D. Davison

Full Paper (10 pages)
Official ACM published version: http://dx.doi.org/10.1145/2556195.2556237
Author's version: PDF (611KB)

Sponsored search is the primary business for today's commercial search engines. Accurate prediction of the Click-Through Rate (CTR) for ads is key to displaying relevant ads to users. In this paper, we systematically study the two kinds of contextual factors influencing the CTR: 1) In micro factors, we focus on the factors for mainline ads, including ad depth, query diversity, ad interaction. 2) In macro factors, we try to understand the correlations of clicks between organic search and sponsored search. Based on this data analysis, we propose novel click models which harvest these new explored factors. To the best of our knowledge, this is the first paper to examine and model the effects of the above contextual factors in sponsored search. Extensive experiments on large-scale real-world datasets show that by incorporating these contextual factors, our novel click models can outperform state-of-the-art methods.

In Proceedings of the 7th Annual ACM International Conference on Web Search and Data Mining (WSDM), pages 113-122, New York City, NY, February 2014.

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Last modified: 6 January 2014
Brian D. Davison