Abstract

We present a social tag recommendation model for collaborative

bookmarking systems.

Suggesting most relevant tags for a given URL and its description.

We are using Lucene index and probability based appraoch to determine

the same.

Problem Statement

Design a tag recommendation system which will form a tag cloud from a

given corpus.

The tag recommendation problem can be described as follows: For a given

post P whose user is U and resource is R, a set of tags are suggested as tags

for the post. Here we denote post as P, tag as T, resource as R, user as U.

Related Work

Some of the previous work in tag recommendation area has been done in

content-based and collaborative approach.

In the content-based approach, a system exploits some textual source with

Information Retrieval-related techniques in order to extract relevant

unigrams or bigrams from the text.

Approach

We started with some pre-processing of given training dataset.

First we crawl the URLs from given training dataset to extract the web

content like text, pdf, html document etc.

Than we use Lucene to Index the crawled data.

We are using similarity score based approach and probability based

approach to identify most relevant tags for given query for the same we

are creating one more index other than previous one.

Approach… (continued)

For the Extraction of candidate tags we are using following sources::

URL given by the user

From the user's previously tagged resources

From the given description

Word related tags which are extracted from description

From similar resources

For Ranking we are using user history and applying a probabilistic approach

Architecture

Theory

As a part of our probabilistic model we are calculating probability on following

different events:

Tag popularity for link

Tag popularity in user's tag

Popularity of tag over all the data

How much tag is related to words in given description

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 “Aldo Moro”, Italy

{musto,narducci,degemmis,lops,semeraro}@di.uniba.it

Tag recommendation by machine learning with textual and social features

Xian Chen · Hyoseop Shin Received: 5 February 2011 / Revised: 4 January

2012 / Accepted: 5 March 2012 / Published online: 1 April 2012 © Springer

Science+Business Media, LLC 2012

A Tag Recommendation System based on contents Ning Zhang, Yuan

Zhang, and Jie Tang Knowledge Engineering Group Department of

Computer Science and Technology, Tsinghua University, Beijing, China

[email protected], [email protected] and

[email protected]

AutoTag: A Collaborative Approach to Automated Tag Assignment for

Weblog Posts Gilad Mishne ISLA, University of Amsterdam Kruislaan 403,

1098SJ Amsterdam, The Netherlands

Thank You !

Selection of Tags for Tag Clouds

Technology

Transcript of Selection of Tags for Tag Clouds