On Group Extraction and Fusion for Tag-Based Social Recommendation

Publisher:
IGI Global
Publication Type:
Chapter
Citation:
Social Media Mining and Social Network Analysis: Emerging Research, 2013, 1st, pp. 211 - 223
Issue Date:
2013-01
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With the recent information explosion, social websites have become popular in many Web 2.0 applications where social annotation services allow users to annotate various resources with freely chosen words, i.e., tags, which can facilitate users finding preferred resources. However, obtaining the proper relationship among user, resource, and tag is still a challenge in social annotation-based recommendation researches. In this chapter, the authors aim to utilize the affinity relationship between tags and resources and between tags and users to extract group information. The key idea is to obtain the implicit relationship groups among users, resources, and tags and then fuse them to generate recommendation. The authors experimentally demonstrate that their strategy outperforms the state-of-the-art algorithms that fail to consider the latent relationships among tagging data.
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