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.
- Facebook Statistics, http://www.facebook.com/press/info.php?statistics.Google Scholar
- The Open Directory Project (ODP), http://www.dmoz.org.Google Scholar
- E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In SIGIR '06. Google ScholarDigital Library
- H. Bao and E. Y. Chang. Adheat: an influence-based diffusion model for propagating hints to match ads. In WWW '10. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003. Google ScholarDigital Library
- D. Boyd and N. B. Ellison. Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 2007.Google ScholarDigital Library
- D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. In WWW '08. Google ScholarDigital Library
- P. Chatterjee, D. L. Hoffman, and T. P. Novak. Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science, 22, 2003. Google ScholarDigital Library
- M. Ciaramita, V. Murdock, and V. Plachouras. Online learning from click data for sponsored search. In WWW '08. Google ScholarDigital Library
- M. Daoud, L. Tamine, M. Boughanem, and B. Chebaro. Learning implicit user interests using ontology and search history for personalization. In WISE'07: Proceedings of the 2007 international conference on Web information systems engineering. Google ScholarDigital Library
- D. Fensel, F. van Harmelen, I. Horrocks, D. L. McGuinness, and P. F. Patel-Schneider. Oil: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16, 2001. Google ScholarDigital Library
- J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 2001.Google Scholar
- D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM, 35, 1992. Google ScholarDigital Library
- Q. Gu, J. Zhou, and C. H. Q. Ding. Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs. In SDM, 2010.Google ScholarCross Ref
- G. King and L. Zeng. Logistic regression in rare events data. Political Analysis, 9:137--163, 2001.Google ScholarCross Ref
- I. Konstas, V. Stathopoulos, and J. M. Jose. On social networks and collaborative recommendation. In SIGIR '09. Google ScholarDigital Library
- A. Y. Ng. Feature selection, l1 vs. l2 regularization, and rotational invariance. In ICML '04: Proceedings of the twenty-first international conference on Machine learning. Google ScholarDigital Library
- B. Piwowarski and H. Zaragoza. Predictive user click models based on click-through history. In CIKM '07. Google ScholarDigital Library
- F. Provost, B. Dalessandro, R. Hook, X. Zhang, and A. Murray. Audience selection for on-line brand advertising: privacy-friendly social network targeting. In KDD '09. Google ScholarDigital Library
- B. Ribeiro-Neto, M. Cristo, P. B. Golgher, and E. Silva de Moura. Impedance coupling in content-targeted advertising. In SIGIR '05. Google ScholarDigital Library
- A. Shepitsen, J. Gemmell, B. Mobasher, and R. Burke. Personalized recommendation in social tagging systems using hierarchical clustering. In RecSys '08. Google ScholarDigital Library
- D. Vallet, M. Fernández, P. Castells, P. Mylonas, and Y. Avrithis. Personalized information retrieval in context. In 3rd International Workshop on Modeling and Retrieval of Context, 2006.Google Scholar
- C. Wang, P. Zhang, R. Choi, and M. D'Eredita. Understanding consumers attitude toward advertising. In Eighth Americas Conference on Information Systems, pages 1143--1148, 2002.Google Scholar
- Z. Wen and C.-Y. Lin. On the quality of inferring interests from social neighbors. In KDD '10. Google ScholarDigital Library
- R. W. White, P. Bailey, and L. Chen. Predicting user interests from contextual information. In SIGIR '09. Google ScholarDigital Library
Index Terms
- Learning relevance from heterogeneous social network and its application in online targeting
Recommendations
How much can behavioral targeting help online advertising?
WWW '09: Proceedings of the 18th international conference on World wide webBehavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking ...
Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?
Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of ...
An economic analysis of online advertising using behavioral targeting
Online publishers and advertisers have recently shown increasing interest in using targeted advertising online. Such targeting allows them to present users with advertisements that are a better match, based on their past browsing and search behavior and ...
Comments