Abstract
In sponsored search, the ad selection algorithm is used to pick out the best candidate ads for ranking, the bid keywords of which are best matched to the user queries. Existing ad selection methods mainly focus on the relevance between user query and selected ads, and consequently the monetization ability of the results is not necessarily maximized. To this end, instead of making selection based on keywords as a whole, our work takes advantages of the different impacts, as revealed in our data study, of different components inside the keywords on both relevance and monetization ability. In particular, we select keyword components and then maximize the relevance and revenue on the component level. Finally, we combine the selected components to generate the bid keywords. The experiments reveal that our method can significantly outperform two baseline algorithms on the metrics including recall, precision and the monetization ability.
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Hui, K., Gao, B., He, B., Luo, Tj. (2013). Sponsored Search Ad Selection by Keyword Structure Analysis. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_20
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DOI: https://doi.org/10.1007/978-3-642-36973-5_20
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