TY - GEN
T1 - Learning relevance from heterogeneous social network and its application in online targeting
AU - Wang, Chi
AU - Raina, Rajat
AU - Fong, David
AU - Zhou, Ding
AU - Han, Jiawei
AU - Badros, Greg
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Clickthrough prediction
KW - Cognitive relevance
KW - Heterogeneous social networks
KW - Online advertising
UR - http://www.scopus.com/inward/record.url?scp=80052111504&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052111504&partnerID=8YFLogxK
U2 - 10.1145/2009916.2010004
DO - 10.1145/2009916.2010004
M3 - Conference contribution
AN - SCOPUS:80052111504
SN - 9781450309349
T3 - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 655
EP - 664
BT - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011
Y2 - 24 July 2011 through 28 July 2011
ER -