TY - GEN
T1 - Argo
T2 - 17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
AU - Wang, Xin-Jing
AU - Yu, Mo
AU - Zhang, Lei
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2009
Y1 - 2009
N2 - Though monetizing user-generated photos has a great potential in image business, this topic is seldom touched due to the difficulties of both image understanding and ads-to-images vocabulary matching. In this technical demonstration, we show case the Argo system, which attempts to monetize UGC (user-generated content) photos by mining a user's interest from a group of his photos and advertising the photos accordingly. Given a page of photos, it first auto-tags each photo by a large-scale search-based image annotation method, then maps both image annotations and the textual descriptions of ads onto an ODP-based topic hierarchy. The mapping produces semantic features which are statistical distributions on ODP topics. Ads are ranked by their similarities to such topic distributions of the photos and the top-ranked ones are output as suggested ads.
AB - Though monetizing user-generated photos has a great potential in image business, this topic is seldom touched due to the difficulties of both image understanding and ads-to-images vocabulary matching. In this technical demonstration, we show case the Argo system, which attempts to monetize UGC (user-generated content) photos by mining a user's interest from a group of his photos and advertising the photos accordingly. Given a page of photos, it first auto-tags each photo by a large-scale search-based image annotation method, then maps both image annotations and the textual descriptions of ads onto an ODP-based topic hierarchy. The mapping produces semantic features which are statistical distributions on ODP topics. Ads are ranked by their similarities to such topic distributions of the photos and the top-ranked ones are output as suggested ads.
KW - Image understanding
KW - Photo monetization
KW - User interest modeling
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-72449200601&origin=recordpage
U2 - 10.1145/1631272.1631466
DO - 10.1145/1631272.1631466
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781605586083
T3 - MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
SP - 957
EP - 958
BT - MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
Y2 - 19 October 2009 through 24 October 2009
ER -