This document summarizes a tag recommender system for social bookmarking. It discusses the dataset used, the approach which involves creating an index with Lucene and training an SVM model on feature vectors. It outlines the main modules of the system including an indexer, SVM, user input, ranking, and tag cloud generator modules. Future work aims to handle queries with terms not in the corpus.

1. Tag Recommender For Social
Bookmarking
Ashutosh Chaudhary 201205607
Neha Ummareddy 201156208
Sushant Makode 201305562
Vidit Gupta 201101099

2. Agenda

Overview

Dataset

Approach

Modules

Future Work

References

Conclusion

3. Overview

We present a smart tag recommender

Able to learn from past user interaction

Content of the resources to annotate.

4. Dataset

We use a CSV file containing

approximately 1116834 entries

along with around 235805 users in it.

5. Approach

We create an index using Lucene of the entire
dataset.

iterate on the entire dataset and create feature
vectors for each url

create an SVM model using these feature
vectors

6. Approach (Contd)

We parse the query and find it's feature vectors

We use our SVM model to retrieve relevant
results

We input the relevant results and the weight
assigned to them and get a tag cloud as output

7. Modules

Indexer Module

SVM Module

User Input Module

Ranking Module

Tag Cloud Generator Module

8. Future work

Performance of system decreases when query
contains terms that are not present in our
corpus

To overcome this we can find partial matches in
the urls we have and then suggest tags using
them. For this we have to split the given url in
small parts and use dynamic programming to
find the most appropriate split.

9. References

AutoTag: A Collaborative Approach to Automated Tag Assignment for
Weblog Posts Gilad Mishne ISLA, University of Amsterdam Kruislaan
403, 1098SJ Amsterdam, The Netherlands

R. Baeza-Yates and B. Ribeiro-Neto.Modern Informatio Retrieval.
Addison- Wesley, 1999.

STaR: a Social Tag Recommender System Cataldo Musto, Fedelucio
Narducci, Marco de Gemmis, Pasquale Lops, and Giovanni
Semeraro

TagAssist: Automatic Tag Suggestion for Blog Posts, Sanjay C. Sood,
Sara H. Owsley, Kristian J. Hammond, Larry Birnbaum

10. Conclusion

This is a powerful tool for tag recommendation
for social bookmarking and it overcomes the
problems in existing tag recommender systems
with the use of SVM learning.