Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2008
DOI: 10.1145/1401890.1402015
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Learning from multi-topic web documents for contextual advertisement

Abstract: Contextual advertising on web pages has become very popular recently and it poses its own set of unique text mining challenges. Often advertisers wish to either target (or avoid) some specific content on web pages which may appear only in a small part of the page. Learning for these targeting tasks is difficult since most training pages are multi-topic and need expensive human labeling at the sub-document level for accurate training. In this paper we investigate ways to learn for sub-document classification wh… Show more

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Cited by 33 publications
(14 citation statements)
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“…As shown in previous studies, strong relevance increases the number of click-throughs [1] [3] [10] [13]. Some studies [6] [14] have also demonstrated that focusing on relevant topics written with positive sentiment produces high click-through rates. Although a page-relevant topic is a way to capture visitors' interest, there is no other way to determine their personal interests.…”
Section: Introductionmentioning
confidence: 69%
“…As shown in previous studies, strong relevance increases the number of click-throughs [1] [3] [10] [13]. Some studies [6] [14] have also demonstrated that focusing on relevant topics written with positive sentiment produces high click-through rates. Although a page-relevant topic is a way to capture visitors' interest, there is no other way to determine their personal interests.…”
Section: Introductionmentioning
confidence: 69%
“…In [40,125,129,130], MIL was used to infer the sentiment expressed in individual sentences based on the labels provided for entire user reviews. MIL has also been used to discover relations between named entities [11].…”
Section: Document Classification and Web Miningmentioning
confidence: 99%
“…Such an approach is generally enhanced with additional models that sustain the semantic similarity between web pages and advertisements (Broder, 2007); (Zhang et al, 2008); (Ribeiro-Neto et al, 2005). This association generates a semantic score which, combined with the lexical, consolidates the match.…”
Section: Problem Statementmentioning
confidence: 99%