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• W2059792214 abstract "Contextual advertising seeks to place relevant textual ads within the content of generic webpages. In this article, we explore a novel semantic approach to contextual advertising. This consists of three tasks: (1) building a well-organized hierarchical taxonomy of topics, (2) developing a robust classifier for effectively finding the topics of pages and ads, and (3) ranking ads based on the topical relevance to pages. First, we heuristically build our own taxonomy of topics from the Open Directory Project (ODP). Second, we investigate how to increase classification accuracy by taking the unique characteristics of the ODP into account. Last, we measure the topical relevance of ads by applying a link analysis technique to the similarity graph carefully derived from our taxonomy. Experiments show that our classification method improves the performance of Ma- F 1 by as much as 25.7% over the baseline classifier. In addition, our ranking method enhances the relevance of ads substantially, up to 10% in terms of precision at k , compared to a representative strategy." @default.
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• W2059792214 date "2013-10-01" @default.
• W2059792214 modified "2023-10-16" @default.
• W2059792214 title "Semantic contextual advertising based on the open directory project" @default.
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• W2059792214 doi "https://doi.org/10.1145/2529995.2529997" @default.
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