After a quite intensive week, WWW has concluded and it’s seems a good time to look back and summarize the main activities and experiences. Though it was my intention to keep a dairy log of the events, the lack of time was much more important than I expected. So, let this late entry replace the originally planned ones 😉
WWW started today with the round of tutorials and workshops that commonly precede such large conferences. I met again with some of the people I’ve been running into in the last few conferences I’ve attended and used the chance to receive an update from them. I also met with my former college Stefan Siersdorfer, co-author of the submission I got accepted into this conference.
My joint work with Stefan Siersdorfer “Ranking and Classifying Attractiveness of Photos in Folksonomies” has been accepted as a full paper in the next edition of ACM Conference on the World Wide Web (WWW’09).
In this paper, we propose a novel methodology to derive a metric of photo attractiveness in a completely automatic manner taking advantage of user generated data in Flickr (namely metadata and user feedback statistics) as well as image visual features. The paper can be downloaded from the conference site using the following link. Slides for the presentation will be soon available from the same site.
Find below the abstract of the submission:
“Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. The growing size of these folksonomies poses new challenges in terms of search and data mining. In this paper we introduce a novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members. To this end, we exploit image features known for having signiﬁcant effects on the visual quality perceived by humans (e.g. sharpness and colorfulness) as well as textual meta data, in what is a multi-modal approach. Using feedback and annotations available in the Web 2.0 photo sharing system Flickr, we assign relevance values to the photos and train classiﬁcation and regression models based on these relevance assignments. With the resulting machine learning models we categorize and rank photos according to their attractiveness. Applications include enhanced ranking functions for search and recommender methods for attractive content. Large scale experiments on a collection of Flickr photos demonstrate the viability of our approach.”