Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Machine Learning
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Expertise drift and query expansion in expert search
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Competition-based user expertise score estimation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Tag-based social image retrieval: An empirical evaluation
Journal of the American Society for Information Science and Technology
Inferring who-is-who in the Twitter social network
Proceedings of the 2012 ACM workshop on Workshop on online social networks
A social inverted index for social-tagging-based information retrieval
Journal of Information Science
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We propose a method for finding impressive creators in online social network sites (SNSs). Many users are actively engaged in publishing their own works, sharing visual content on sites such as YouTube or Flickr. In this paper, we focus on the Japanese illustration-sharing SNS, Pixiv. We implement an illustrator search system based on user impression categories. The impressions of illustrators are estimated from clues in the crowdsourced social-tag annotations on their illustrations. We evaluated our system in terms of normalized discounted cumulative gain and found that using feedback on motifs and impressions for illustrations of relevant illustrators improved illustrator search by 11%.