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Optimizing Web Search Using Social Annotations. Shanghai JiaoTong University IBM China Research Lab. Stated Problem/Why interesting?. Proposed Solution. SocialSimRank to calculate similarity between annotations and query. Recent studies:.

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Optimizing Web Search Using Social Annotations

Shanghai JiaoTong University IBM China Research Lab

Stated Problem/Why interesting?

Proposed Solution

SocialSimRank to calculate similarity between annotations and query

Recent studies:

  • Ranking web pages based on similarity (vector-space model)
  • Ranking web pages based on quality (PageRank)
  • Utilizing social annotations (folksonomies, Semantic Web)

Trivial approach:

Better solution:

First to propose integrating social annotations with traditional web search

SocialPageRank to calculate quality in terms of annotation count

My Opinion

  • Similar to PageRank: “quality” is passed between the users, annotations and pages until convergence
  • Annotations are descriptive, but not “clean” summaries (synonymy problem)
  • Not very clear exactly how to incorporate both algorithms into web search (not the point of the paper?)
  • Web’s adversarial nature ignored, tag spamming only briefly mentioned
  • Paper is interesting and readable; known concepts applied in novel way. We could’ve thought of it! It was our corn to cut!

Connection with Course Material

  • Including new measure into basic similarity formula
  • SocialSimRank label disambiguation approach (similar to SemTag/SemSeeker)
  • SocialPageRank concept identical to PageRank
  • Use of annotation correlation matrices analogous to term-term matrices