1. CS246 Search Engine Bias

2. Junghoo "John" Cho (UCLA Computer Science)2 Motivation “If you are not indexed by Google, you do not exist on the Web” --- news.com article  People “discover” pages through search engines  Top results: many users  Bottom results: no new users  Are we biased by search engines?

3. Junghoo "John" Cho (UCLA Computer Science)3 Research issues  Are we biased by search engines?  Impact of Search Engines on Page Popularity  Can we avoid search engine bias?  Page Quality: In Search of Unbiased Web Ranking  Shuffling a Stacked Deck: The Case for Partially Randomized Ranking of Search Engine Results

4. Junghoo "John" Cho (UCLA Computer Science)4 Questions to Address  Are the rich getting richer?  Web popularity evolution experiments  How much bias do search engines introduce?  Web user models and popularity evolution analysis  Any potential solution to the problem?  Less biased ranking metric  Introducing randomness to search results

5. Junghoo "John" Cho (UCLA Computer Science)5 Web Evolution Experiment  Collect Web history data  Is “rich-get-richer” happening?  From Oct. 2002 until Oct. 2003  154 sites monitored  Top sites from each category of Open Directory  Pages downloaded every week  All pages in each site  A total of average 4M pages every week (65GB)

6. Junghoo "John" Cho (UCLA Computer Science)6 “Rich-Get-Richer” Problem  Construct weekly Web-link graph  From the downloaded data  Partition pages into 10 groups  Based on initial link popularity  Top 10% group, 10%-20% group, etc.  How many new links to each group after a month?  Rich-get-richer  More new links to top groups

7. Junghoo "John" Cho (UCLA Computer Science)7 Result: Simple Link Count  After 7 months  70% of new links to the top 20% group  No new links to bottom 60% groups

8. Junghoo "John" Cho (UCLA Computer Science)8 Result: PageRank  After 7 months  Decrease in PageRank for bottom 50% pages  Due to normalization of PageRank

9. Junghoo "John" Cho (UCLA Computer Science)9 Impact of Search Engines  Yes, the rich seems to get richer, but is it because of search engine?  Even further, is it really a “bias”?  Study of bias from search engine is necessary

10. Junghoo "John" Cho (UCLA Computer Science)10 Search Engine Bias  What we mean by bias?  What is the ideal ranking? How do search engines rank pages?

11. Junghoo "John" Cho (UCLA Computer Science)11 What is the Ideal Ranking?  Rank by intrinsic “quality” of a page?  Very subjective notion  Different quality judgment on the same page  Can there be an “objective” definition?

12. Junghoo "John" Cho (UCLA Computer Science)12 Page Quality Q(p)  The probability that an average Web user will like page p if he looks at it  In principle, we can measure Q(p) by 1.showing p to all Web users and 2.counting how many people like it  p1: 10,000 people, 8,000 liked it, Q(p1) = 0.8  p2: 10,000 people, 2,000 liked it, Q(p2) = 0.2  Democratic measure of quality  When consensus is hard to reach, pick the one that more people like

13. Junghoo "John" Cho (UCLA Computer Science)13 PageRank: Practical Ranking  A page is “important” if many pages link to it  Not every link is equal  A link from an “important” page matters more than others  PR(pi) = (1 - d) + d [PR(p1)/c1 + · · · + PR(pm)/cm]  Random-Surfer Model  When users follow links randomly, PR(pi) is the probability to reach pi

14. Junghoo "John" Cho (UCLA Computer Science)14 PageRank vs. Quality  PageRank ~ Page quality if everyone is given equal chance  High PageRank  high quality  To obtain high PageRank, many people should look at the page and like it.  Low PageRank  low quality?  PageRank is biased against new pages  How much bias for low PageRank pages?

15. Junghoo "John" Cho (UCLA Computer Science)15 Measuring Search Engine Bias  Ideal experiment: Divide the world into two groups  The users who do not use search engines  The users who use search engines very heavily  Compare popularity evolution  Problem: Difficult to conduct in practice

16. Junghoo "John" Cho (UCLA Computer Science)16 Theoretical Web-User Model  Let us do theoretical experiments!  Random-surfer model  Users follow links randomly  Never use serach engine  Search-dominant model  Users always start with a search engine  Only visit pages returned by search engine  Compare popularity evolution

17. Junghoo "John" Cho (UCLA Computer Science)17 Basic Definitions  Simple popularity P(p,t)  Fraction of Web users who like p at time t  E.g., 100,000 users, 10,000 like p, P(p,t)=0.1  Visit popularity V(p,t)  # users that visit p in a unit time  Awareness A(p,t)  Fraction of Web users who are aware of p  E.g, 100,000 users, 30,000 aware of p, A(p,t)=0.3  P(p,t) = Q(p) A(p,t)

18. Junghoo "John" Cho (UCLA Computer Science)18 Random-Surfer Model  Popularity-equivalence hypothesis  V(p,t) = r P(P,t) (r: proportionality constant)  Rationale: PageRank is visit popularity under the random-surfer model  Random-visit hypothesis  A visit done by any user with equal probability  Simplifying assumption

19. Junghoo "John" Cho (UCLA Computer Science)19 Random-Surfer Model: Analysis  Current popularity P(p,t)  Number of visitors from V(p,t) = r P(p,t)  Awareness increase ∆A(p,t)  Popularity increase ∆P(p,t)  New popularity P(p,t+1)  Formal analysis: Differential equation

20. Junghoo "John" Cho (UCLA Computer Science)20 Random-Surfer Model: Result  The popularity of page p evolves over time as  Q(p): quality of p  P(p,0): initial popularity of p at time zero  N: total number of Web users  R: proportionality constant

21. Junghoo "John" Cho (UCLA Computer Science)21 Random-Surfer Model: Popularity Evolution Q(p)=1 P(p,0)=10^-8 r/n = 1

22. Junghoo "John" Cho (UCLA Computer Science)22 Search-Dominant Model  V(p,t) ~ P(p,t)?  For i th result, how many clicks?  For PageRank P(p,t), what ranking?  Empirical measurements  New Visit-popularity hypothesis V(p,t) = r P(p,t) 9/4  Random-visit hypothesis

23. Junghoo "John" Cho (UCLA Computer Science)23 Search-Dominant Model: Popularity Evolution Same parameter as before

24. Junghoo "John" Cho (UCLA Computer Science)24 Comparison of Two Models  Time to final popularity  66 times increase!  Expansion stage  Random surfer: 12 time units  Search dominant: non existent Random-surfer modelSearch-dominant model

25. Junghoo "John" Cho (UCLA Computer Science)25 Reducing the Bias?  Many possibilities!  Can we measure quality?  Will randomness help?  Show some random pages in search results  Give a new page a chance

26. Junghoo "John" Cho (UCLA Computer Science)26 Measuring Quality: Basic Idea  Quality: probability of link creation by a new visitor  Assuming the same number of visitors  Q(p)  # of new links (or popularity increase) Quality estimator Q(p) =  P(p)

27. Junghoo "John" Cho (UCLA Computer Science)27 Measuring Quality: Problem (1)  Different number of visitors to each page  More visitors to more popular pages  How to account for # of visitors? Quality estimator Q(p) =  P(p) / P(p)  Idea: PageRank = # of visitors  Divide by current PageRank

28. Junghoo "John" Cho (UCLA Computer Science)28 Measuring Quality: Problem (2)  No more new links to very popular pages  Everyone already knows them  P(p) / P(p) ~ 0 for well-known pages  How to account for well-known pages? Quality estimator Q(p) =  P(p) / P(p) + C  P(p)  Idea: P(p) = Q(p) when everyone knows p  Use P(p) to measure Q(p) for well-known pages

29. Junghoo "John" Cho (UCLA Computer Science)29 Quality Estimator: Theory  Under the random-surfer model, Q(p) is  Essentially the same as the previous formula Q(p) =  P(p) / P(p) + C  P(p)

30. Junghoo "John" Cho (UCLA Computer Science)30 Is Quality Estimator Effective?  How to measure its effectiveness?  Implement it to a major search engine?  Any other alternatives?  Idea  Pages eventually obtain deserved popularity (however long it may take…)  “Future” PageRank ~ Q(p)

31. Junghoo "John" Cho (UCLA Computer Science)31 Quality Estimator: Evaluation  Q(p) as a predictor of future PageRank  Compare the correlations of  Current Q(p) with future PageRank  Current PageRank with future PageRank  Does Q(p) predicts future PageRank better? PR’(p) Q(p) PR(p) ?  Experiments  Download Web multiple times with long interval

32. Junghoo "John" Cho (UCLA Computer Science)32 Quality Estimator: Evaluation  Compare relative error  Result  For Q(p): err(p) = 0.45  For PR(p): err(p) = 0.74  Q(p) is significantly better than PR(p)

33. Junghoo "John" Cho (UCLA Computer Science)33 Quality Estimator: Detail

34. Junghoo "John" Cho (UCLA Computer Science)34 Randomization  Let us give new pages a chance to prove themselves  Introduce randomness in search results  Say, 10% of results are randomly selected from new pages  Why is randomization good?  New high-quality pages will be promoted quickly  But is it really important?  Counter argument  Most new pages are bad  Why should users bother looking at them?

35. Junghoo "John" Cho (UCLA Computer Science)35 Average Quality Per Click  Bottom line: User’s satisfaction  Make sure users like the pages they click  Tradeoff of randomization  Positive: High-quality new pages will become popular more quickly  Improvement in search quality  Negative: Randomly selected pages are likely to be of low quality  Decrease in search quality

36. Junghoo "John" Cho (UCLA Computer Science)36 Exploration/Exploitation Tradeoff

37. Junghoo "John" Cho (UCLA Computer Science)37 Joke Experiments (1)  Ranked list of jokes  Users click on a link and read a joke  Provide positive or negative feedback  “Simulated search”  Two ranked lists and user groups 1.Popularity-based: ranked by # of positive votes 2.Popularity + randomization  1000 users participated

38. Junghoo "John" Cho (UCLA Computer Science)38 Joke Experiments (2)  Ranking determines the popularity evolution of jokes  Compare the evolution and evaluate  Evaluation metric  Fraction of positive user votes  Result  Popularity only: 0.2  Popularity + randomization: 0.35

39. Junghoo "John" Cho (UCLA Computer Science)39 More Analytical Study  Based on search-dominant user model  But pages get created and deleted over time  In most cases, 10-20% randomization is helpful  Optimal randomness depends on exact parameter settings

40. Junghoo "John" Cho (UCLA Computer Science)40 Summary  Search engine bias  Do search engines make popular pages more popular?  Experimental and analytical study  Strong possibility  Possible solutions  Less biased ranking metric  Randomization in search results