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Journal of the ACM (JACM)
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The Journal of Machine Learning Research
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CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Marketing Science
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WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
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SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise networks in online communities: structure and algorithms
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DiffusionRank: a possible penicillin for web spamming
SIGIR '07 Proceedings of the 30th 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
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Proceedings of the 18th international conference on World wide web
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Proceedings of the 18th international conference on World wide web
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Who should share what?: item-level social influence prediction for users and posts ranking
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Wisdom of the better few: cold start recommendation via representative based rating elicitation
Proceedings of the fifth ACM conference on Recommender systems
Social and behavioural media access
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Measuring two-event structural correlations on graphs
Proceedings of the VLDB Endowment
Speeding up large-scale learning with a social prior
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Affinity-driven blog cascade analysis and prediction
Data Mining and Knowledge Discovery
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In this paper, we present AdHeat, a social ad model considering user influence in addition to relevance for matching ads. Traditionally, ad placement employs the relevance model. Such a model matches ads with Web page content, user interests, or both. We have observed, however, on social networks that the relevance model suffers from two shortcomings. First, influential users (users who contribute opinions) seldom click ads that are highly relevant to their expertise. Second, because influential users' contents and activities are attractive to other users, hint words summarizing their expertise and activities may be widely preferred. Therefore, we propose AdHeat, which diffuses hint words of influential users to others and then matches ads for each user with aggregated hints. We performed experiments on a large online Q&A community with half a million users. The experimental results show that AdHeat outperforms the relevance model on CTR (click through rate) by significant margins.