A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Marketing Science
Impedance coupling in content-targeted advertising
Proceedings of the 28th 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
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
Automatic identification of pro and con reasons in online reviews
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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
Learning from multi-topic web documents for contextual advertisement
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Hi-index | 0.00 |
This paper addresses the concept of Blogger-Centric Contextual Advertising, which refers to the assignment of personal ads to any blog page, chosen in according to bloggers' interests. As blogs become a platform for expressing personal opinions, they naturally contain various kinds of statements, including facts, comments and statements about personal interests, of both a positive and negative nature. To extend the concept behind the Long Tail theory in contextual advertising, we argue that web bloggers, as the constant visitors of their own blog sites, could be potential consumers who will respond to ads on their own blogs. Hence, in this paper, we propose using text mining techniques to discover bloggers' immediate personal interests in order to improve online contextual advertising. The proposed BCCA (Blogger-Centric Contextual Advertising) framework aims to combine contextual advertising matching with text mining in order to select ads that are related to personal interests as revealed in a blog and rank them according to their relevance. We validate our approach experimentally using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method could effectively identify those ads that are positively-correlated with a blogger's personal interests.