Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Data-driven text features for sponsored search click prediction
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Learning the click-through rate for rare/new ads from similar ads
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Pattern based keyword extraction for contextual advertising
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
SmartAds: bringing contextual ads to mobile apps
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
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Computational advertising, popularly known as Online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the web. It is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem attacked in computational advertising (CA) is of the match making between the ads and the context. Based on the context, CA can be broadly compartmentalized into following three areas: Sponsored search, Contextual advertising and Social advertising. Sponsored search refers to the placement of ads on search results page. Contextual advertising deals with matching advertisements to the third party web pages. We refer the placements of ads on a social networking page, leveraging user's social contacts as social advertising. My research work aims at leveraging various user interactions, ad and advertiser related information and contextual information for these three areas of advertising. The research work focuses on the identification of various factors that contribute in retrieving and ranking the most relevant set of ads that match best with the context. Specifically, information associated with the user, publisher and advertiser is leveraged for this purpose.