2008/06/06 Y.H.Chang Towards Effective Browsing of Large Scale Social Annotations1 Towards Effective Browsing of Large Scale Social Annotations WWW 2007

2008/06/06 Y.H.ChangTowards Effective Browsing of Large

Scale Social Annotations1

Towards Effective Browsing of Large Scale Social Annotations

WWW 2007

Advisor: Hsin-Hsi ChenReporter: Y.H Chang

2008-06-06

Rui Li, Shenghua Bao, Yong Yu, Zhong Su, and Ben Fei

Shanghai JiaoTong University IBM China Research Lab



Outline

• Introduction

• ELSABer overview

• Components of ELSABer

• Enhanced models

• Experimental results

• Conclusion



Introduction

• Today, a lot of services (e.g., Del.icio.us, Filckr) have been provided for helping users to manage and share their favorite URLs and photos based on social annotations.

• How to effectively find desired resources from large annotation data is a new problem.

• In this paper, we propose a novel algorithm, namely Effective Large Scale Annotation Browser (ELSABer), to browse large-scale social annotation data.



Introduction

• ELSABer helps the users browse huge number of annotations in a semantic, hierarchical and efficient way.

• By incorporating the personal and time information, ELSABer can be further extended for personalized and time-related browsing.



A set of pages

related to the current annotation“programming”

The prototype system based on ELSABer

Sub-tags (sub category) of “programming”



ELSABer overview• Input An empty concept set SC

• Step 1 Output the initial view of annotations– generates TOP 100 tags from 2000 most frequently URLs and tags. – They are the roots in hierarchical browsing.

• Loop User select a tag Ti

• Step 2 Concept Matching– Add tag Ti to set SC

– Calculate related tag set and URL set• Step 3 (optional) sample URL set and sample Tag set• Step 4 Hierarchical Browsing

– 4-1 Calculate candidate sub-tags– 4-2 Rank the sub-tags by Infor-score

• IF Termination condition Satisfied; Return• ELSE Loop



Components of ELSABer

• Data setup and representation• Semantic Browsing

– a. Annotation Similarity Estimation

– b. Generating the Semantic Concept

• Hierarchical Browsing– c. Sub-Tag Generation

– d. Sub-Tag Clustering

• Efficient Browsing



Data setup and representation

• Del.icio.us (May, 2006)• We define an annotation as a quadruple:

– (User, URL, Tag, Time).• Associated matrix Mmxn

• m and n is the total number of tags and URLs• |URL(ti)| represents the number of URLs annotated by tag ti.• Cij denote the number of users who annotate the jth URL with

the ith tag

Like the TFIDF of IR



Data setup and representation

• Given the associated matrix Mmxn :

T1

T2

.

.

.

Tm

the tag can be represented as a row vector Ti (U1,U2,.. Un) of M

the URL can be represented as a column vector Ui (t1,t2,…,tm) of M.

U1 U2 .. … .. Un



Semantic Browsinga. Annotation Similarity Estimation

• Similarity:

• Special case-1(stemming):

Ex: Programs & Programming

=> add 0.1 weight

• Special case-2(punctuation):

Ex: Web-dev & WebDev

=> add 0.08 weight



Semantic Browsingb. Generating the Semantic Concept

• Given the selected tag ti, we choose a tag set STi that is most related to ti by following rules:– 1. tj should be among the N most similar tags relat

ed to ti– 2. The similarity should be larger than a threshold

θ.– N=4, θ=0.7

• semantic concept Ci = STi {ti}∪



Semantic Browsingb. Generating the Semantic Concept

• The path of user’s clicking: t1, t2,…,tL will bring a sequence of concepts: C1, C2,…,CL.

• Let concept set SC = {C1, C2,…, CL}.

• The related URLs :– ReURL(SC ) = {u | C S∀ ∈ C ,T(u) ∩C ≠ Φ}– T(u) means the set of annotations given to URL u.

• the related tags can be defined as all the tags given to ReURL(SC ):– ReTag(SC ) {t | u ReURL(S∈ C ),t T(u)}∈



Hierarchical Browsingc. Sub-Tag Generation

• If the intersection URL set is the main part of all the URLs of ti, but a small part of tj, we can infer that ti is a sub-tag of tj

40 related tags of “google”



Hierarchical Browsingc. Sub-Tag Generation

Features Features

Coverage of Tags

ICR

Intersection Rate

IR’

IRR Top 1~30 =1 (by IR rank)

Top 30~60 =2

Top 60~ =3

U(ti) denotes the number of URLs tagged with ti



Hierarchical Browsing c. Sub-Tag Generation

• Given the features above, each related tag is represented as a feature vector. A decision tree can be derived from the manually labeled data set to predict the sub-tag relations using C4.5.



Hierarchical Browsingd. Sub-Tag Clustering



Hierarchical Browsingd. Sub-Tag Clustering

• Infor(t) = w1TFIDF(t) + w2ICS(t) + w3TE(t) • Intra-Cluster Similarity:

– ot denotes the centroid of all the URLs associated with the tag

• Tag Entropy:

• In our experiment, these weights are 0.58, 0.27, and 0.13, respectively.



Efficient Browsing

• Observation : People use popular tags to annotate URLs and also the popular URLs are annotated by the majority of tags.



Efficient Browsing

• So we can get good results efficiently by running our algorithm in a small sub tagging space .

• In our experiment, we sampling 2000 most frequently annotated URLs and 2000 most frequently tag , so the size of M is 2000 × 2000

• <we do not cut off the “long tail”>

• After a sequence of click by the user, the intention of the user will be more specific, this causes a decreasing number of related URLs or related tags.

• When the number is less than 2000, all the tags and URLs will be calculated



Enhanced Models

• User’s profile:• The user interested annotations and resources can be f

ound as follows:

• Ri denotes the vector representation of a resource, and Ti denotes the vector representation of Ai.

• Adjust the sampling and ranking algorithms according to the user’s preference:– Infor (t,U) = α × Infor (t) + β ×UI (t | P(U))



Enhanced Models

Given the user required time interval TI= [ts, te]. We define the match of the URL’s time sequence TS and the user required time interval TI as follows:

θ=0.5



Experiment results

• The scale of the dataset:

• Machine: Intel Pentium IV 3.0 GHz, 1GB memory, 2 processors

• Java • Lucene API is also used to build URL and Tag index.

Del.icio.us (May, 2006)

1,736,268 web pages

269,566 different annotations



Experiment results

Red tag: owned by user

Orange tag: recommended



Experiment results



Conclusion

• Our main contributions:• The proposal of the effective algorithm – ELSABer b

ased on the analysis of social annotation’s characteristics.

• The proposal of enhanced models for personalized and time related browsing.



Future work

• more user studies• emphasize on how to find more qualified URL resour

ces• utilize existing hierarchical structures such as ODP an

d WordNet for helping construct more meaningful hierarchical structures for social annotations.



• Thank you!!

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2008/06/06 Y.H.Chang Towards Effective Browsing of Large Scale Social Annotations1 Towards Effective Browsing of Large Scale Social Annotations WWW 2007