2011 ScalableDistributedInferenceofD

• (Ahmed et al., 2011) ⇒ Amr Ahmed, Yucheng Low, Mohamed Aly, Vanja Josifovski, and Alexander J. Smola. (2011). “Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020433

Subject Headings: User Profiling.

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Scalable+Distributed+Inference+of+Dynamic+User+Interests+for+Behavioral+Targeting
• http://dl.acm.org/citation.cfm?id=2020408.2020433&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; computational advertising; distributed inference; experimentation; large-scale; learning; online inference; user modeling

Abstract

Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully unsupervised fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current.

We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 ScalableDistributedInferenceofD	Alexander J. Smola Amr Ahmed Yucheng Low Mohamed Aly Vanja Josifovski			Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting				10.1145/2020408.2020433		2011