1. 2009/05/04 Y.H.Chang 1Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks Advisor: Hsin-Hsi Chen Reporter: Y.H Chang 2009-05-04 Takashi Menjo, Masatoshi Yoshikawa Graduate School of Information Science Nagoya University Workshop SWSM 2008

2. 2009/05/04 Y.H.Chang 2Trend 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.

3. 2009/05/04 Y.H.Chang 3Trend 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

4. 2009/05/04 Y.H.Chang 4Trend 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]] =[(u 1, t 1, L 1 ), (u 2, t 2, L 2 ),… (u n, t n, L n )] The page p is first be bookmarked at time t 1.

5. 2009/05/04 Y.H.Chang 5Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks Modeling bookmarks Velocity of bookmark at t 1 +iτ Acceleration –a i ’ = v i+1 -v i (i=1, 2, …,l-1) smoothing: Cumulative number of bookmarks

6. 2009/05/04 Y.H.Chang 6Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks Evaluation of bookmark growth Given parameters: α,β,γ

7. 2009/05/04 Y.H.Chang 7Trend 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)

8. 2009/05/04 Y.H.Chang 8Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks Trend prediction using values of users

9. 2009/05/04 Y.H.Chang 9Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks p1, p2, p3, p4

10. 2009/05/04 Y.H.Chang 10Trend 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 time interval I and was given l )

11. 2009/05/04 Y.H.Chang 11Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks Parameters

12. 2009/05/04 Y.H.Chang 12Trend 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:

13. Experiment 2009/05/04 Y.H.Chang 13Trend 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 t now ) (rank M (p):The ranking of page p in the set M)

14. 2009/05/04 Y.H.Chang 14Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks

15. 2009/05/04 Y.H.Chang 15Trend 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)

16. 2009/05/04 Y.H.Chang 16Trend 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

17. 2009/05/04 Y.H.Chang 17Trend Prediction in Social Bookmark Service Using Time Series of Bookmarks Thank you!!