Term-weighting approaches in automatic text retrieval
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Unintrusive customization techniques for Web advertising
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OCELOT: a system for summarizing Web pages
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ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
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Web-page classification through summarization
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Impedance coupling in content-targeted advertising
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Predicting clicks: estimating the click-through rate for new ads
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IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Efficient query expansion for advertisement search
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Blogger-centric contextual advertising
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The anatomy of an ad: structured indexing and retrieval for sponsored search
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Using landing pages for sponsored search ad selection
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A contextual-bandit approach to personalized news article recommendation
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A large-scale active learning system for topical categorization on the web
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A Wikipedia Matching Approach to Contextual Advertising
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Blogger-Centric Contextual Advertising
Expert Systems with Applications: An International Journal
Learning website hierarchies for keyword enrichment in contextual advertising
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Leveraging Wikipedia concept and category information to enhance contextual advertising
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Advertising Keywords Recommendation for Short-Text Web Pages Using Wikipedia
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Keeping keywords fresh: a BM25 variation for personalized keyword extraction
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An adaptive approach to chinese semantic advertising
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Multimedia Tools and Applications
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Information Sciences: an International Journal
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HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: users and contexts of use - Volume Part III
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Contextual Advertising is a type of Web advertising, which, given the URL of a Web page, aims to embed into the page (typically via JavaScript) the most relevant textual ads available. For static pages that are displayed repeatedly, the matching of ads can be based on prior analysis of their entire content; however, ads need to be matched also to new or dynamically created pages that cannot be processed ahead of time. Analyzing the entire body of such pages on-the-fly entails prohibitive communication and latency costs. To solve the three-horned dilemma of either low-relevance or high-latency or high-load, we propose to use text summarization techniques paired with external knowledge (exogenous to the page) to craft short page summaries in real time. Empirical evaluation proves that matching ads on the basis of such summaries does not sacrifice relevance, and is competitive with matching based on the entire page content. Specifically, we found that analyzing a carefully selected 5% fraction of the page text sacrifices only 1%-3% in ad relevance. Furthermore, our summaries are fully compatible with the standard JavaScript mechanisms used for ad placement: they can be produced at ad-display time by simple additions to the usual script, and they only add 500-600 bytes to the usual request.