Searching and Browsing Using Tags

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Searching and Browsing Using Tags

• Nikos Sarkas
• Social Information Systems Seminar
• DCS, University of Toronto, Winter 2007

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Social Resource Sharing

• The del.icio.us paradigm.
• Users store links to web pages of interest along
  with arbitrary, user-specified tags in a server.
• The model is independent of the resource being
  shared.
• Music (Last.fm)
• Photos (Flickr)
• Publications (CiteULike)

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• Part I Searching

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Ranking Web Search Results

• Two prevalent models.
• Ranking based on query-document similarity.
• TF/IDF
• Metadata extraction
• Link analysis
• Query independent static ranking.
• PageRank
• Quality based

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Similarity Ranking, Take I

• Query qq1,q2,,qn.
• Tags of URL p, T(p)t1,t2,,tm.
• Define similarity as qnT(p)/T(p).
• Problems
• Synonymy (according to the authors)
• Others?
• Synonymy example
• Linux, Ubuntu and Gnome

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Similarity Ranking, Take II

• Use tags with similar meaning to enrich query.
• Create 3 matrices
• MTP, tag-URL count matrix
• ST, tag-tag similarity matrix
• SP, URL-URL similarity matrix

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Similarity Ranking, Take II

• Iterate
• Similarly update SP, until convergence.
• Then, similarity between a query q and a url p is

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Social PageRank

• Popular web pages are tagged by many up-to-date
  users, using hot tags.
• Transfer popularity between entities.
• Define matrices MPU, MUT, MTP.
• Iterate

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Putting It All Together

• Train a ranking function (RankSVM) using the
  following features
• BM25 similarity between query and url content
• Simple query-url tags similarity measure
• Complex query-url tags similarity measure
• PageRank
• Social PageRank
• Results
• Precision, NDCG at k
• Small improvement over BM25, up to 25 for NDCG
  and synthetic queries

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• Part II Browsing

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Tag Assisted Browsing

• Currently two methods for tag driven browsing
• Keyword search
• Clouds of popular tags
• We would like to support
• Semantic browsing also present URLs annotated
  with similar tags
• Hierarchical browsing browse in a top-down
  fashion

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Semantic Browsing

• Define similarity between tags
• Synonymic tags similarity above a threshold.
• The synonymic tags and the tag itself defines its
  semantic concept.
• Given that the user has selected L tags, that
  define semantic concepts ScC1,,CL, related
  URLs are

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Hierarchical Browsing

• Observations
• No neat tree structure
• Multiple ways to target resource
• URLs associated with different categories
• Dynamic structure leafs can become inner nodes

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Hierarchical Browsing

• Generating sub-tags
• Train a classifier to identify which of the tags
  in the semantic concept are sub-tags
• Features used ratio of tag counts, intersection
  size, etc.
• Clustering sub-tags
• Ranks tags based on a complex formula
• Greedy clustering technique