tag-based social interest discovery by yjhuang 2008.5 yahoo! inc searcher xin li, lei guo,...

Tag-based Social Interest Discovery

By yjhuang2008.5

Yahoo! Inc Searcher Xin Li, Lei Guo, Yihong(Eric) Zhao

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Outline

Introduction Data Set Analysis of Tags The Architecture Evaluation

Introduction

Social network systems Del.icio.us, Facebook, MySpace, Youtube

Discovering Social Interests Main challenge

Difficult to detect and represent Existing approaches: online connections

This paper’s work

Based on user-generated tags Analyze the real-world traces of tags

and web content Develop the Internet Social Interest

Discovery system (ISID) Discover the common user interests Cluster users and urls by topics

Evaluation

Data Set

Delicious Bookmark 4.3m bookmarks, 0.2m users, 1.4m urls

Data Collection and Pre-Processing

Crawl the urls & download the url pages Discard all non-html objects Coding -> UTF-8, remove non-English

pages Stopword List Porter Stemming algorithm 298,350 distinct tags, 4,072,265

keywords

Users, URLs and Tags

Figure 1: Distribution of the frequencies that the

URLs were bookmarked in our data set Log-log scale


Figure 2: Distribution of the bookmarking activity Log-log scale


Figure 3: Distribution of tag frequencies

Analysis of Tags

Use VSM model Each URL: two vectors

One in the space of all tags, one for doc keywords

A corpus with t terms and d documents A term-document matrix A = . .

Weight Measurements

Tf-based

Tf-Idf based

An Example of Tags vs. Keywords

A URL bookmarked by users About the resolv.conf in Linux

Table show the top 10 keywords

The Vocabulary of Tags

Compare the vocabulary of tags with that of keywords in web documents

if the most import words be covered Figure 4 (5)

The coverage of user-generated tags for the tf (tf-idf) keywords of 7000 random docs.

The Convergence of Tag Selections

Measure the convergence of tags for all URLs

X-axis: the popularity of URLs Y-axis: the no. of distinct tags

Tags Matched by Documents

Tags: catch the main concept of docs? Matched by the content of the URL?

Statistical analysis Occurrences no. -> weight Tag match ration e(T, U)

T= ti: the set of tags attached to a

given URL U

The total weight of the tags that also appeared in the keyword set

of U

Tags Matched by Documents

Architecture for Social Interest Discovery

1.Find topics of interests

2.Clustering

3.Indexing

Topic Discovery

Find frequent tag pattern for a given set The association rule algorithms

Support Implication rules Identify the frequent tag patterns a frequent tag pattern {a,b}

If w({a,b}) = w({a}) = w({b})

Clustering

Indexing

Evaluation

The URL Similarity of Intra- and Inter- Topics Cosine similarity of tf-idf keyword term vector Cosine similarity of Tag tem vector 500 interest topics

> 30 bookmarked urls Share 5-6 co-occurring tags

Inter-: 10,000 topic-pairs

User Interest Coverage

For each user Sort his tags by the number of times the

tags have been used by the user

Top-5: the top 5 hot tags of each user Top-10: All:

Human Reviews

4 human editors 10 topics 20 most frequent urls for each topic Scores: 1-5

Cluster Properties(Add)

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Conclusion(Add)

Propose a tag-based social interest discovery approach

Justify user-generated tags to represent user interests

Implement a system in social network such as delicious

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References

Xin Li, Lei Guo, Yihong Zhao, Tag-based Social Interest Discovery, www08, Yahoo! Inc

備註投影片下載出處：http://fusion.grids.cn/wiki/download/att

achments/1313/Tag-based+Socail+Interest+Discovery-by+yjhuang.ppt?version=1

Data Set 網頁http://delicious.com/