Review-oriented metadata enrichment: a case study

Authors:
Liang Zhang;Jiangqin Wu;Yueting Zhuang;Yin Zhang;Chenxing Yang
Affiliations:
College of Computer Science , Zhejiang University, Hangzhou, China;College of Computer Science , Zhejiang University, Hangzhou, China;College of Computer Science , Zhejiang University, Hangzhou, China;College of Computer Science , Zhejiang University, Hangzhou, China;College of Computer Science , Zhejiang University, Hangzhou, China
Venue:
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Year:
2009

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Abstract

Book reviews contributed by readers in social sites contain valuable information on books' content, style and merit, many informative words in which can be used to enrich metadata of books in China-Us Million Book Digital Library. In this paper, we present a system for review-oriented metadata enrichment and propose an Book-Centric Diverse Random Walk algorithm on a four-partite graph containing three kinds of relations among authors, books, reviews and words, in order to produce highly relevant as well as diverse keywords for a book. Experimental results of a user study show that our approach significantly outperforms other methods in terms of relevance and diversity. The metadata generated by our approach also has a large overlap with popular social tags and brief introductions from DouBan for books in the coverage experiments.