CLARE: A joint approach to label classification and tag recommendation

Yilin Wang, Suhang Wang, Jiliang Tang, Guojun Qi, Huan Liu, Baoxin Li

Research output: Contribution to conferencePaper

19 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages210-216
Number of pages7
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: Feb 4 2017Feb 10 2017

Other

Other31st AAAI Conference on Artificial Intelligence, AAAI 2017
CountryUnited States
CitySan Francisco
Period2/4/172/10/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Wang, Y., Wang, S., Tang, J., Qi, G., Liu, H., & Li, B. (2017). CLARE: A joint approach to label classification and tag recommendation. 210-216. Paper presented at 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, United States.