In this paper, we introduce a system named Argo which provides intelligent advertising made possible from users' photo collections. Based on the intuition that user-generated photos imply user interests which are the key for profitable targeted ads, the Argo system 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.
['Xin-Jing Wang', 'Mo Yu', 'Lei Zhang', 'Rui Cai', 'Wei-Ying Ma']
Argo: intelligent advertising by mining a user's interest from his photo collections