Learning relevance from heterogeneous social network and its application in online targeting

Chi Wang, Rajat Raina, David Fong, Ding Zhou, Jiawei Han, Greg Badros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The rise of social networking services in recent years presents new research challenges for matching users with interesting content. While the content-rich nature of these social networks offers many cues on "interests" of a user such as text in user-generated content, the links in the network, and user demographic information, there is a lack of successful methods for combining such heterogeneous data to model interest and relevance. This paper proposes a new method for modeling user interest from heterogeneous data sources with distinct but unknown importance. The model leverages links in the social graph by integrating the conceptual representation of a user's linked objects. The proposed method seeks a scalable relevance model of user interest, that can be discriminatively optimized for various relevance-centric problems, such as Internet advertisement selection, recommendation, and web search personalization. We apply our algorithm to the task of selecting relevant ads for users on Facebook's social network. We demonstrate that our algorithm can be scaled to work with historical data for all users, and learns interesting associations between concept classes automatically. We also show that using the learnt user model to predict the relevance of an ad is the single most important signal in our ranking system for new ads (with no historical clickthrough data), and overall leads to an improvement in the accuracy of the clickthrough rate prediction, a key problem in online advertising.

Original languageEnglish (US)
Title of host publicationSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages655-664
Number of pages10
ISBN (Print)9781450309349
DOIs
StatePublished - Jan 1 2011
Event34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011 - Beijing, China
Duration: Jul 24 2011Jul 28 2011

Publication series

NameSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011
CountryChina
CityBeijing
Period7/24/117/28/11

Keywords

  • Clickthrough prediction
  • Cognitive relevance
  • Heterogeneous social networks
  • Online advertising

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Wang, C., Raina, R., Fong, D., Zhou, D., Han, J., & Badros, G. (2011). Learning relevance from heterogeneous social network and its application in online targeting. In SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 655-664). (SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval). Association for Computing Machinery. https://doi.org/10.1145/2009916.2010004