An algorithm for suffix stripping
Readings in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A vector space model for automatic indexing
Communications of the ACM
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Just-in-time contextual advertising
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A noisy-channel approach to contextual advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
Approximate entity extraction in temporal databases
World Wide Web
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
A semantic approach to recommending text advertisements for images
Proceedings of the sixth ACM conference on Recommender systems
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Contextual advertising is an important part of today's Web. It provides benefits to all parties: Web site owners and an advertising platform share the revenue, advertisers receive new customers, and Web site visitors get useful reference links. The relevance of selected ads for a Web page is essential for the whole system to work. Problems such as homonymy and polysemy, low intersection of keywords and context mismatch can lead to the selection of irrelevant ads. Therefore, a simple keyword matching technique gives a poor accuracy. In this paper, we propose a method for improving the relevance of contextual ads. We propose a novel "Wikipedia matching" technique that uses Wikipedia articles as "reference points" for ads selection. We show how to combine our new method with existing solutions in order to increase the overall performance. An experimental evaluation based on a set of real ads and a set of pages from news Web sites is conducted. Test results show that our proposed method performs better than existing matching strategies and using the Wikipedia matching in combination with existing approaches provides up to 50% lift in the average precision. TREC standard measure bpref-10 also confirms the positive effect of using Wikipedia matching for the effective ads selection.