Position-wise contextual advertising: Placing relevant ads at appropriate positions of a web page
|Position-wise contextual advertising: Placing relevant ads at appropriate positions of a web page|
|Author(s)||Wu Z., Xu G., Lu C., Chen E., Zhang Y., Zhang H.|
|Keyword(s)||Contextual advertising, Similarity, Wikipedia knowledge (Extra: Contextual advertisings, Insertion position, Marketing channels, Online advertising, Semantic features, Service information, Similarity, Wikipedia knowledge, Internet, Semantics, Websites, Marketing, advertizing, article, concept mapping, global positioning system, image display, image processing, Internet, knowledge discovery, mathematical computing, online analysis, priority journal, scoring system, web browser)|
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Position-wise contextual advertising: Placing relevant ads at appropriate positions of a web page is a 2013 journal article written in English by Wu Z., Xu G., Lu C., Chen E., Zhang Y., Zhang H. and published in Neurocomputing.
Web advertising, a form of online advertising, which uses the Internet as a medium to post product or service information and attract customers, has become one of the most important marketing channels. As one prevalent type of web advertising, contextual advertising refers to the placement of the most relevant ads at appropriate positions of a web page, so as to provide a better user experience and increase the user's ad-click rate. However, most existing contextual advertising techniques only take into account how to select as relevant ads for a given page as possible, without considering the positional effect of the ad placement on the page, resulting in an unsatisfactory performance in ad local context relevance. In this paper, we address the novel problem of position-wise contextual advertising, i.e., how to select and place relevant ads properly for a target web page. In our proposed approach, the relevant ads are selected based on not only global context relevance but also local context relevance, so that the embedded ads yield contextual relevance to both the whole target page and the insertion positions where the ads are placed. In addition, to improve the accuracy of global and local context relevance measure, the rich wikipedia knowledge is used to enhance the semantic feature representation of pages and ad candidates. Last, we evaluate our approach using a set of ads and pages downloaded from the Internet, and demonstrate the effectiveness of our approach. © 2013 Elsevier B.V.
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