Abraham Bagherjeiran

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Understanding what interests and delights users is critical to effective behavioral targeting, especially in information-poor contexts. As users interact with content and advertising, their passive behavior can reveal their interests towards advertising. Two issues are critical for building effective targeting methods: what metric to optimize for and how to(More)
There are two main requirements for effective advertising in social networks. The first is that links in the social network are relevant to the targeted ads. The second is that social information can be easily incorporated with existing targeting methods to predict response rates. Our purpose in this paper is to investigate these requirements. We measure(More)
Performance advertisers want to maximize the return on their advertising spend. In the online advertising world, this means showing the ad only to those users most likely to convert i.e. buy a product or service. Existing ad targeting solutions such as context targeting and rule-based segment targeting primarily leverage marketing intuition to identify(More)
Adaptive clustering uses reinforcement learning to learn the reward values of successive data clusterings. Adaptive clustering applies when external feedback exists for a clustering task. It supports the reuse of clusterings by memorizing what worked well in a previous context. It explores multiple paths in a reinforcement learning environment when the goal(More)
Adaptive clustering uses external feedback to improve cluster quality; past experience serves to speed up execution time. An adaptive clustering environment is proposed that uses Q-learning to learn the reward values of successive data clusterings. Adaptive clustering supports the reuse of clusterings by memorizing what worked well in the past. It has the(More)
Online advertising offers significantly finer granularity, which has been leveraged in state-of-the-art targeting methods, like Behavioral Targeting (BT). Such methods have been further complemented by recent work in Look-alike Modeling (LAM) which helps in creating models which are customized according to each advertiser's requirements and each campaign's(More)