Topic Modeling in Social Media by nehalwan

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									Tag Recommendation in Social
Bookmarking sites like Deli.cio.us




    Varun Ahuja (201206628)
    Vinay Singri (201305592)
    Tanuj Sharma ( 201101138 )
   Introduction
Automated   process of suggesting
 relevant keywords given a
 dataset


Given  link L, description D, and
 user U, a set of personalized tags
 CT(L) are suggested with help
 from given dataset.
First Approach – STaR ( Social Tag
Recommender System )
Divided in 3 major steps – Pre-processing,
 Indexing and Recommendation
Pre-processing– Remove useless tags, Case
 Folding, Spam Removal
Indexing   – Index existing tags against users.
Recommendation    – Combine outputs of Title to
 Tag, Resource Profile, User Profile
 Recommender.
  Problems in First Approach


Notall tags from the dataset
 appeared.


Low   Precision and Low Recall


Without  crawling the given link,
 this approach gives low accuracy
 Final Approach – Supervised   Learning Model

Modelled as a ranking problem of
 candidate tags of a given URL


Consists   of 3 stages –
 ◦ Candidates Tag Extraction
 ◦ SVM Features Construction
 ◦ Ranking Process


Ranking    SVM is used for ranking candidate
 tags.
 Candidates Tag Extraction
Extracted   from –
 ◦ Description field of link L
 ◦ Tags assigned by the same user U
   previously
 ◦ Tags to assigned to the same link L by other
   users


Given   link L, user U, candidate tags
 CT{L} = { description(L) union Tags(U) union
Tags(L) }
    SVM Features Construction
5 features used for each Candidate Tag ( CT ) –
Candidate   Tag's Term Frequency (TF) in link's description
 terms
Candidate   Tag's Term Frequency (TF) in link's URL terms
Candidate Tag’s Term Frequency (TF) in T{Rj} (tags
 assigned to the same URL in the training data).
Candidate Tag’s Term Frequency (TF) in T{Ui} (tags
 assigned previously by user in the training data.)
Times  of candidate tag being assigned as a tag in the
 training data.
          Ranking
Forany link in test dataset, Candidate
 Tags are extracted


Features   stored for each candidate tag.


SVM  ranking model ranks the candidate
 tags from top to bottom


Top   K tags selected
Tools Used
    Future Work

Extension   to various datasets


      more enriched
Giving
 recommendation for the seed URL


Candidate Tags can be expanded
 using content similarity based KNN
 model.
            References
 STaR:  a Social Tag Recommender System Cataldo
    Musto, Fedelucio Narducci, Marco de Gemmis,
    Pasquale Lops, and Giovanni Semeraro
 Department of Computer Science, University of
Bari, Italy


•   Social Tag Prediction Base on
    Supervised Ranking Model
  Hao Cao, Maoqiang Xie, Lian Xue, Chunhua Liu, Fei
Teng and Yalou Huang
  College of Software, Nankai University, Tianjin,
P.R.China

								
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