 Users annotate things (resources) with labels (tags)  These annotations are shared, creating a collaborative dataset called a folksonomy coffee java.

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Presentation transcript:

 Users annotate things (resources) with labels (tags)  These annotations are shared, creating a collaborative dataset called a folksonomy coffee java User A User B programming

◦ With some knowledge about a tagging user, we want to present that user with new information:  Given a resource, what are some tags that the user may find appropriate for annotation?  Given a tag (or set of tags), what are some relevant resources the user may find interesting?  Generally, what resources may interest the user?  What other users have similar interests to this user?  The first of these has been the focus of most recommender research.

 Task: Recommend tags during the annotation process  Benefits ◦ Reduce user effort ◦ Encourage tags more frequently ◦ Encourage the application of more tags ◦ Reuse common tags ◦ Offer tags the user had not previously considered ◦ Reduce noise created by capitalization inconsistencies, spelling errors and other discrepancies.  Results: A cleaner denser dataset that is useful in its own right or for future data mining techniques.

UR UT TR

 Memory-based ◦ Simple Popularity contest ◦ K-Nearest Neighbor, adapted from CF  Model-based ◦ Link Analysis (Adapted PageRank, FolkRank) ◦ Latent Concept Models  LSA (using SVD or Hebbian deflation)  pLSA (using learned belief networks  Knowledge-based ◦ Use external ontology to relate tags/resources

 What makes a “good” tag recommendation?  According to IR tradition, we measure the predictive power of a recommendation algorithm

 Consider the following recommendations for a resource representing a research paper on folksonomies  Is prediction really what we want in a practical recommendation system?