1. 1 Web Search Personalization via Social Bookmarking and Tagging Michael G. Noll & Christoph Meinel Hasso-Plattner-Institut an der Universit¨at Potsdam, Germany ISWC 2007 Advisor: Prof. Hsin-Hsi Chen Reporter: Yu-Hui Chang 2008/07/30

2. 2 Introduction

3. 2008/07/30Y.H. Chang3/27 Social bookmarking and tagging social bookmarking: –publicly sharing your bookmarks with others –including any additional metadata tagging / folksonomies: –Users annotate Documents with with a flat, unstructured list of keywords called Tags

4. 2008/07/30Y.H. Chang4/27 Web search personalization integration of user-specific data to improve results / advertising two main approaches: 1.modify user's query: “nyt” > “new york times” 2. re-rank search results based on user profile

5. 5 Personalization Technique

6. 2008/07/30Y.H. Chang6/27 Personalization Input: –user profile + document profiles Via social bookmarking and tagging Algorithm: 1.calculate Similarity(user, document) for all docs 2.sort documents by similarity from highest to lowest Output: –re-ranked search result list

7. 2008/07/30Y.H. Chang7/27 Profile user profile –“ Tagmarking ” A user search “research internet security” =>he/she can click single button to bookmark a document with auto translate tag “research”, “internet”, ”security” document profiles –Communicating with the bookmarking service over its web API Tagmarking

8. 2008/07/30Y.H. Chang8/27 Personalization Complete process of web search personalization * every step done in real-time

9. 2008/07/30Y.H. Chang9/27 Data Aggregation: User Profile User’s profile example m tags n documents

10. 2008/07/30Y.H. Chang10/27 Data Aggregation: Doc. Profile Document’s profile example m tags n users MuMu

11. 2008/07/30Y.H. Chang11/27 Similarity Similarity(u,d)=p u T ‧ ||p d || –||pd||: simply normalization of document profile, reset matrix element with only 1 and 0 two values ( True or False)

12. 2008/07/30Y.H. Chang12/27 Similarity example Similarity(u,d)=p u T ‧ ||p d || …11…1111………11…1111…… 13 19 2 10 21 34 =13*0+19*1+2*0+10*1+21*0+34*1=63

13. 2008/07/30Y.H. Chang13/27 Similarity score properties the key factor: unmodified user profile –Promotes known and similar doc., demotes those unknown or non-similar doc. more sophisticated normalization for both user and document is on-going Score of unknown document => 0!!! most critical factor in practice: –“do we have sufficient data to make all this work?”

14. 2008/07/30Y.H. Chang14/27 Personalization system setup –server: social bookmarking service –client: browser add-on modification of search engine UI by updating the DOM tree of the search result pages in real-time

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16. 2008/07/30Y.H. Chang16/27 Re-rank example a user with a strong interest in information technology and network security

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18. 18 Experiment and Evaluation

19. 2008/07/30Y.H. Chang19/27 Experiment Del.icio.us –Public social bookmarking service –Large user community Key question: Quantitative analysis –How many social annotations in practice? Qualitative analysis –Quality evaluation

20. 2008/07/30Y.H. Chang20/27 Quantitative analysis test set –140 “popular tags” on del.icio.us –1400 search results link (top 10 results ) totaling –981,989 bookmarks –20,498 tag annotations (2,300 unique)

21. 2008/07/30Y.H. Chang21/27 Quantitative analysis

22. 2008/07/30Y.H. Chang22/27 Quantitative analysis we can expect to personalize approx. 85% (in the 1 st page) per query in practice Percentage of links with at least 1 tag

23. 2008/07/30Y.H. Chang23/27 Qualitative analysis For each query, participants were presented two search result lists: 1.original list from Google Search 2.The personalized version 8 participants evaluate the top 10 results for 13 queries each –Participant’s job: researchers, web masters, software developers, system administrators –The average number of bookmarks for a participant was 153.

24. 2008/07/30Y.H. Chang24/27 Qualitative analysis Some discussions: –“Expert” user profiles –Disambiguate words and contexts Personalized version Better Worse Equal

25. 25 Conclusion

26. 2008/07/30Y.H. Chang26/27 Conclusion proposed personalization approach is feasible and viable in practice: –already sufficient user-supplied metadata available –initial evaluation of personalization quality shows very promising results Open Access: http://www.michael-noll.com/dmoz100k06/ - data set http://www.michael-noll.com/delicious-api/ - scripts

27. 27 Thank you!!