The Journal of Machine Learning Research
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Marketing Science
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '06 Proceedings of the 29th 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
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Lexical Graphs for Improved Contextual Ad Recommendation
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Modern Information Retrieval
A Wikipedia Matching Approach to Contextual Advertising
World Wide Web
A Hidden Topic-Based Framework toward Building Applications with Short Web Documents
IEEE Transactions on Knowledge and Data Engineering
Leveraging Wikipedia concept and category information to enhance contextual advertising
Proceedings of the 20th ACM international conference on Information and knowledge management
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Contextual advertising is a type of online advertising in which the placement of commercial ads within a web page depends on the relevance of the ads to the page content. A common approach to determine relevance is to score the match between ads and the content of the viewed page, for example, by simple keyword or syntactic matching. However, because of the sparseness of advertising language and the lack of context, this approach often leads to the selection of irrelevant ads. In this paper, we propose using topic modeling to improve the relevance of retrieved ads. Unlike existing methods that directly model the content of an ad as a distribution over topics, the proposed method uses a keyword-topic model that associates each keyword provided by the advertiser with a multinomial distribution over topics. Then, an ad with multiple keywords is represented as a mixture of topic distributions associated with those keywords. We empirically evaluated the performance of the proposed method on a set of real ads and web pages. The results show that using the keyword-topic model gives improved accuracy over traditional keyword matching and a topic modeling methods that do not include information about keyword-topic association. Further, combining the keyword-topic model with other methods yields extra increase in ad recommendation accuracy.