A Collaborative Filtering Tag Recommendation System Based On Graph

A Collaborative Filtering Tag Recommendation System
based on Graph
Yuan Zhang, Ning Zhang, and Jie Tang
Knowledge Engineering Group
Department of Computer Science and Technology, Tsinghua University, Beijing, China
fancyzy0526@gmail.com, zntsinghua1117@gmail.com and
jietang@tsinghua.edu.cn

Abstract. With the rapid development of web2.0 technologies, tagging become
much more importanttoday to organize information and help users search the
information they need with social bookmarking tools. In order to fini sh the
second task of ECML PKDD challenge 2009, we propose a graph -based
collaborative filtering tag recommendation system. We also refer to an
algorithm called FolkRank, which is an adaptation of the famous Page Rank.
We evaluate and compare these two approaches andshow that a combination of
these two methods will perform better results for our task.

1

Introduction

Tagging is very useful for users to figure out other users with similar interests within
a given category. Users with similar interests might p ost similar tags and similar
resources might have similar tags posted to them. Collaborative filtering is widely
used in automaticprediction system. The idea behind it is very simple: those who
agreed in the past tend to agree again in the future. Traditional collaborative filtering
systems have two steps. The first step is to look for users who share the same rating
patterns with the active user whom the prediction is for. Then, the systems will use
the ratings from those like-minded users found in the first step to calculate aprediction for the active user. Since all the tags, users and resources in the test data
are also in the training file, we can make use of the history of users ’ tag, also called
personomy[3] and tags previously posted to the resource to recommend tags for a
active post. This paper presents our proposed tag recommendation system, which is a
combination of two methods: one is an adaption ofitem-based collaborative filtering,
the other is FolkRank according to [4,5].
As we mentioned above, collaborative filtering performs well for automatic
prediction. However, current widely used collaborative filtering systems are for
predicting the ratings of some products or recommend some products to users. For
example, the famous websites, Amazon.com1, Last.fm2, eBay3 apply this method to1

http://www.amazon.com

their recommendation systems. Our first method considers the tags previously posted
to the resource and users’ similarities to recommend tags. The second method is an
application of the FolkRank algorithm in [4, 5].
These two methods have some common features. They both use the history of the
user and tags previously posted to resource for recommendation. Theyare both
suitable to the case that test data are in the training data. Both of them do not need to
establish models in advance. But they are different to some extents. The first method
just considers tags in the candidate set while the FolkRank will consider all the tags in
the training data. Moreover, the first method focuses more on collaborative
information while the second focuses onthe graph information.
This paper is organized as follows: Section 2 introduces recent trends in the area of
social bookmark tag recommendation systems. Section 3 describes our proposed
system and the combination method in details. In Section 4, we present and evaluate
our experimental results on the test data of ECML PKDD challenge 2009 and make
some conclusions in Section 5.

2

Relatedwork

Some researchers have already used some approaches based on collaborative
information for tag recommendation systems. For example, AutoTag[7] and
TagAssist[6] make use of information retrieval skills to recommend tags for weblog
posts. They recommend tags based on the tags posted to the similar weblogs. Our first
method is similar to these t wo approaches.
FolkRank in[4, 5] is a...