Welcome to the InfoSci Platform
Argo: Intelligent Advertising Made Possible from Images

Argo: Intelligent Advertising Made Possible from Images

Xin-Jing Wang, Mo Yu, Lei Zhang, Wei-Ying Ma
Copyright: © 2011 |Pages: 17
ISBN13: 9781609601898|ISBN10: 1609601890|EISBN13: 9781609601911
DOI: 10.4018/978-1-60960-189-8.ch005
Cite Chapter Cite Chapter

MLA

Wang, Xin-Jing, et al. "Argo: Intelligent Advertising Made Possible from Images." Online Multimedia Advertising: Techniques and Technologies, edited by Xian-Sheng Hua, et al., IGI Global, 2011, pp. 67-83. https://doi.org/10.4018/978-1-60960-189-8.ch005

APA

Wang, X., Yu, M., Zhang, L., & Ma, W. (2011). Argo: Intelligent Advertising Made Possible from Images. In X. Hua, T. Mei, & A. Hanjalic (Eds.), Online Multimedia Advertising: Techniques and Technologies (pp. 67-83). IGI Global. https://doi.org/10.4018/978-1-60960-189-8.ch005

Chicago

Wang, Xin-Jing, et al. "Argo: Intelligent Advertising Made Possible from Images." In Online Multimedia Advertising: Techniques and Technologies, edited by Xian-Sheng Hua, Tao Mei, and Alan Hanjalic, 67-83. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-189-8.ch005

Export Reference

Mendeley
Favorite

Abstract

In this chapter, we introduce the Argo system which provides intelligent advertising made possible from user generated photos. Based on the intuition that user-generated photos imply user interests which are the key for profitable targeted ads, Argo attempts to learn a user’s profile from his shared photos and suggests relevant ads accordingly. To learn a user interest, in an offline step, a hierarchical and efficient topic space is constructed based on the ODP ontology, which is used later on for bridging the vocabulary gap between ads and photos as well as reducing the effect of noisy photo tags. In the online stage, the process of Argo contains three steps: 1) understanding the content and semantics of a user’s photos and auto-tagging each photo to supplement user-submitted tags (such tags may not be available); 2) learning the user interest given a set of photos based on the learnt hierarchical topic space; and 3) representing ads in the topic space and matching their topic distributions with the target user interest; the top ranked ads are output as the suggested ads. Two key challenges are tackled during the process: 1) the semantic gap between the low-level image visual features and the high-level user semantics; and 2) the vocabulary impedance between photos and ads. We conducted a series of experiments based on real Flickr users and Amazon.com products (as candidate ads), which show the effectiveness of the proposed approach.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.