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

Varun Ahuja (201206628)Vinay Singri (201305592)Tanuj Sharma ( 201101138 )

IntroductionAutomated 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

Not all 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.

RankingFor 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

Tools Used

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.

ReferencesSTaR: 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