A vector space model for automatic indexing
Communications of the ACM
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A noisy-channel approach to contextual advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
Language model mixtures for contextual ad placement in personal blogs
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Leveraging Wikipedia concept and category information to enhance contextual advertising
Proceedings of the 20th ACM international conference on Information and knowledge management
A keyword-topic model for contextual advertising
Proceedings of the Third Symposium on Information and Communication Technology
Story graphs: Tracking document set evolution using dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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Contextual advertising is a form of online advertising presenting consistent revenue growth since its inception. In this work, we study the problem of recommending a small set of ads to a user based solely on the currently viewed web page, often referred to as content-targeted advertising. Matching ads with web pages is a challenging task for traditional information retrieval systems due to the brevity and sparsity of advertising text, which leads to the widely recognized vocabulary impedance problem. To this end, we propose the use of lexical graphs created from web corpora as a means of computing improved content similarity metrics between ads and web pages. The results of our experimental study provide evidence of significant improvement in the perceived relevance of the recommended ads.