Exploring Online Social Activities for Adaptive Search Personalization

1. Exploring Online Social Activities for Adaptive Search Personalization CIKM’10 Advisor：Jia Ling, Koh Speaker：SHENG HONG, CHUNG

2. Outline • Introduction • System Design • Overview • User Interest Profile • Search Result Personalization • Adaptive adjustment • Evaluation • Conclusion 2

3. Introduction • Social network have experienced explosive growth in the past few years. • Social online activities carry valuable information about users’ background and interests. • How to choose right sources? • Availability • Privacy • Accuracy 3

4. Introduction • In this article, proposing a personalization framework that infers users’ interests and preferences through public activities on a variety of online social systems. • Retrieve information and creates an interest profile for each user. • Based on interest profile to personalize. • Automatically adjust weights of different information. 4

5. ……. ……. ……. Social activity Social activity Social activity Social activity System user user user user ……………………………………………… User Interest profile User Interest profile User Interest profile User Interest profile Personalization Adaptive adjustment

6. System Design---Overview • User Interest Profile • create an interest profile for each user • Receiving a query from a user • Search engine returns a number of webpages • Retrieve interest vector from interest profile • Compute interest score based on how well the webpage matches the user’s interest • Search Result Personalization • Combined both scores into final score • Adaptive Adjustments • Personalization degree • The weights of different social information sources • (relevance score) 5

7. User Interest profile + user Keyword : t Score : s Interest Vector query Cosine similarity Search Engine Search Result Webpage1 Webpage2 Webpage3 Webpage4 Webpage5 . . . Relevance score + Personalization interest score

8. User Interest Profile • Three parts： • Creating interest vectors • Combining interest vectors • Updating interest vectors • Pre-definition： • A user interest profile is represented as {V, W, p} • V : {v1,……,vk} a set of interest vectors • W : {w1,……,wk} weight of the corresponding interest vectors • p : real number called the personalization degree 6

9. 3. Bookmarks 2. Twitter 1. Facebook System user {V, W, p} V = {v1, v2, v3} v1 : user information from Facebook v2 : user information from Twitter v3 : user information from Bookmarks w1 ~ w3 : corresponding weight

10. Creating interest vectors • There are different ways to create an vector • Depending on information source • Text resources： • Keywords：most important keywords • Score：the number of the texts contain this keyword • Tag-based resources： • Keywords：tags are treated as keywords • Score： the number of people have tagged the user with the keyword • For each user, normalize the scores into [0,1] 7

11. Combining interest vectors 8

12. 3. Bookmarks 2. Twitter 1. Facebook 0.3 0.4 0.3 System user Rice(4) Noodle(2) Spaghetti(2) . . . {V, W, p} T1 : { Rice, Noodle, Spaghetti } s(t) = 4*0.4 + 2*0.4 + 2*0.4 = 3.2

13. Updating interest vectors • Periodically crawl new data from social systems • Integrate new information • Add new social information source • Add new interest vector and make use of new data • Give higher probability to new data 9

14. Search Result Personalization • Relevance score • The search engine will then return a list of webpages • 1 / (1+k)：kthwebpage in the result list • Interest score • Cosine similarity between the word vector of the webpage and overall interest vector • Final score • gf(x) = gr(x) * (1-p) + gi(x) * p 10

15. Adaptive adjustment • Adjusting personalization degree • Su：the set of search results that are actually clicked by the user u • Lg ：original list of results returned by the search engine • Lp： final list of result returned by personalized search system 11

16. x : top or x of Lg or Lp ri : 0 or 1 ri = 1 : ith element of L is in S ri = 0 : Otherwise NDCG : Normalized Discounted Cumulative Gain Calculate two values NDCG(Lg , Su ) NDCG(Lp , Su )

17. NDCG(Lg , Su ) = Z3 ( 1/1 + 0 + 0 ) = Z3 NDCG(Lp , Su ) = Z3 ( 1/1 + 1/log23 + 0) = 1… * (Z3) > NDCG(Lg , Su ) NDCG(Lp , Su ) personalization degree

18. Adaptive adjustment • Adjusting source weights • Su：the set of search results that are actually clicked by the user u • vi：the interest vector of the ith information source 12

19. As v1 : Facebook v2 : Twitter h1( v1, Su ) = cos(v1, A) + cos(v1, B) + cos(v1, C) h2( v2, Su ) = cos(v2, A) + cos(v2, B) + cos(v2, C) The average of h = (h1+h2) /2 h1 and h2 which is greater than the average of h ?

20. Evaluation • Experiment • Blogs • Social bookmarks • Mutual tags • 208 users • At least 10 blogs • No less than 10 people tags • Bookmarked 20 webpages or more 13

21. Evaluation Method and Metrics • Use 25% bookmarks to create interest profile • The other 75% is the testing corpus • For ith user ui, randomly choose 30 words • Search query consisting of the word was issued on behalf of ui • Search query consists of a word t • Lt[1,k] is the list of top k results returned by the search system • St is the set of webpages that have been tagged with t by ui 14

22. Evaluation Method and Metrics • Compute the average value of the recall over the 30 search queries issued for ui • Improvement percentage • ra && rb is the average recall of approaches A and B 15

23. Experimental Results • Personalization v.s. Non-personalization 16

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25. Experimental Results • Active users v.s. Less active users 18

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27. Experimental Results • Multiple sources v.s. Single source 20

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29. Experimental Results • Effectiveness of adaption • Personalization degree adjustment (PDA) • Source weight initialization (SWI) • Source weight adjustment (SWA) 22

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31. Conclusion • Propose a personalization framework • Infer users’ preferences from their activities on lots of online social systems • Create user interest profiles • Integrate information from different information resources • How to personalize • Adaptive 24