2009/05/04 y.h.chang 1trend prediction in social bookmark service using time series of bookmarks...

2009/05/04 Y.H.Chang1 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Trend Prediction in Social Bookmark Service Using Time

Series of Bookmarks

Advisor: Hsin-Hsi ChenReporter: Y.H Chang

2009-05-04

Takashi Menjo, Masatoshi YoshikawaGraduate School of Information Science

Nagoya UniversityWorkshop SWSM 2008

2009/05/04 Y.H.Chang2 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Introduction

• Social bookmarking services such as del.icio.us, Hatena Bookmark have been becoming popular in the past few years.

• Bookmarks are categorized by tags in social bookmark services. Tags are short keywords without directories.

2009/05/04 Y.H.Chang3 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Introduction

• Social bookmark services basically rank shared pages by the number of users who have bookmarked the page. However, we believe that there are more useful information for ranking in social bookmark services. – We can… – Take users’ activities into account– anti-spammer, catch the trend topics or interests of

users

2009/05/04 Y.H.Chang4 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Modeling bookmarks

τ:Unit time period (1 hour)

T:time interval, T=lτ(7 days)

Bookmark b=(u, p, t, L)

[[user, page, time, tag sets]]

=[(u1, t1, L1), (u2, t2, L2),… (un, tn, Ln)]

The page p is first be bookmarked at time t1.

2009/05/04 Y.H.Chang5 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Modeling bookmarks

• Velocity of bookmark at t1 +iτ

• Acceleration – ai’ = vi+1-vi (i=1, 2, …,l-1)

smoothing:

• Cumulative number of bookmarks

2009/05/04 Y.H.Chang6 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Evaluation of bookmark growth

•Given parameters: α,β,γ

2009/05/04 Y.H.Chang7 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Trend prediction using values of users

• We consider that the users who noticed the importance of a certain page bookmarked it before it reached some growing intervals.

(users that had bookmarked p before the j-th growth)

•Given parameters: α,β,γ

(page set)

2009/05/04 Y.H.Chang8 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Trend prediction using values of users

Trend prediction using values of users

2009/05/04 Y.H.Chang9 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

p1, p2, p3, p4

2009/05/04 Y.H.Chang10 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Trend prediction using values of users and tags

• We define Prominency of a tag l is below:

( is a set of bookmarks which was shared in a timeinterval I and was given l )

2009/05/04 Y.H.Chang11 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Parameters

2009/05/04 Y.H.Chang12 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Experiment

• From Hatena Bookmark:– 243084 URLs of pages which had been

bookmarked for the first time from February 1, 2007 to March 9, 2007.

• Finally we picked out the top 100 pages from each list :1. Hatena hot list 2. “ User only” ranking list 3. “User and tag” ranking list, to be a set M at the date between 03/01~03/10. Then evaluate it by equations below:

Experiment

•

2009/05/04 Y.H.Chang13 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

(bookmark(p): The number of users who had bookmarked page p in 7 days from tnow)

(rankM(p):The ranking of page p in the set M)

2009/05/04 Y.H.Chang14 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

2009/05/04 Y.H.Chang15 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Experiment results

• Checking the histograms(total 40):

• “User only” is better than Hatena hot list => (26 of 40)

• Similarly, “User and Tag” is better than Hatena hot list =>(24 of 40)

2009/05/04 Y.H.Chang16 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

Conclusion

• In this paper we proposed a trend prediction method using time series of bookmarks.

• Future Work– Introduce interests of the users

2009/05/04 Y.H.Chang17 Trend Prediction in Social Bookmark Service

Using Time Series of Bookmarks

• Thank you!!