TY - CONF
T1 - CLARE
T2 - 31st AAAI Conference on Artificial Intelligence, AAAI 2017
AU - Wang, Yilin
AU - Wang, Suhang
AU - Tang, Jiliang
AU - Qi, Guojun
AU - Liu, Huan
AU - Li, Baoxin
N1 - Funding Information:
Yilin Wang and Baoxin Li were supported in part by an ARO grant (#W911NF1410371) and an ONR grant (#N00014-15-1-2722). G.-J. Qi is partly sponsored by NSF Grant 1560302.Any opinions expressed in this material are those of the authors and do not necessarily reflect the views of ARO, ONR or NSF.
Publisher Copyright:
Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2017
Y1 - 2017
N2 - Data classification and tag recommendation are both important and challenging tasks in social media. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, labels in data classification and tags in tag recommendation are inherently related. For example, a Youtube video annotated with NCAA, stadium, pac12 is likely to be labeled as football, while a video/image with the class label of coast is likely to be tagged with beach, sea, water and sand. The existence of relations between labels and tags motivates us to jointly perform classification and tag recommendation for social media data in this paper. In particular, we provide a principled way to capture the relations between labels and tags, and propose a novel framework CLARE, which fuses data CLAssification and tag REcommendation into a coherent model. With experiments on three social media datasets, we demonstrate that the proposed framework CLARE achieves superior performance on both tasks compared to the state-of-the-art methods.
AB - Data classification and tag recommendation are both important and challenging tasks in social media. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, labels in data classification and tags in tag recommendation are inherently related. For example, a Youtube video annotated with NCAA, stadium, pac12 is likely to be labeled as football, while a video/image with the class label of coast is likely to be tagged with beach, sea, water and sand. The existence of relations between labels and tags motivates us to jointly perform classification and tag recommendation for social media data in this paper. In particular, we provide a principled way to capture the relations between labels and tags, and propose a novel framework CLARE, which fuses data CLAssification and tag REcommendation into a coherent model. With experiments on three social media datasets, we demonstrate that the proposed framework CLARE achieves superior performance on both tasks compared to the state-of-the-art methods.
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UR - http://www.scopus.com/inward/citedby.url?scp=85027836017&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85027836017
SP - 210
EP - 216
Y2 - 4 February 2017 through 10 February 2017
ER -