1. Tag Recommendation in Social
Bookmarking sites like Deli.cio.us
Varun Ahuja (201206628)
Vinay Singri (201305592)
Tanuj Sharma ( 201101138 )

2. 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.

3. 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.

4. Problems in First Approach
Not all tags from the dataset
appeared.
Low Precision and Low Recall
Without crawling the given link,
this approach gives low accuracy

5. 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.

6. 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) }

7. 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.

8. Ranking
For any 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

9. Tools Used

10. Future Work
Extension to various datasets
Giving more enriched
recommendation for the seed URL
Candidate Tags can be expanded
using content similarity based KNN
model.

11. 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