Finding impressive social content creators: searching for SNS illustrators using feedback on motifs and impressions

Yohei Seki, Kiyoto Miyajima

Abstract: 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%.