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SOCIALCOM
2010

Learning to Predict Ad Clicks Based on Boosted Collaborative Filtering

8 years 9 months ago
Learning to Predict Ad Clicks Based on Boosted Collaborative Filtering
This paper addresses the topic of social advertising, which refers to the allocation of ads based on individual user social information and behaviors. As social network services (e.g., Facebook and Morgenstern) are becoming the main platform for social activities, more than 20% of online advertisements appear on social network sites. The allocation of advertisements based on both individual information and social relationships is becoming ever more important. In this study, we first propose the notion of social filtering and compare it with content-based filtering and collaborative filtering for advertisement allocation in a social network. Second, we apply content-boosted and social-boosted methods to enhance existing collaborating filtering models. Finally, an effective learning-based framework is proposed to combine filtering models to improve social advertising. The experiments are conducted based on datasets collected from a social finance web site called Morgenstern. We performed...
Teng-Kai Fan, Chia-Hui Chang
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where SOCIALCOM
Authors Teng-Kai Fan, Chia-Hui Chang
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