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Ao-Jan Su†
Y. Charlie Hu‡
Aleksandar Kuzmanovic†
Cheng-Kok Koh‡
† Northwestern University‡ Purdue University
How to Improve Your Google Ranking:Myths and Reality
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality22
Motivation
● Internet search engines (e.g. Google) drive users to highly ranked pages
● Search engines ranking results greatly influence how people acquire knowledge from the Internet [Pan ‘07]
● It is desirable to understand how a search engine ranks web pages
● Search engines’ ranking algorithms are proprietary■ Publicly available information is very limited and out-
dated
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality33
Current Approaches
● Guess-works by webmasters■ Trial and error■ Inefficient
● Based on experience of search engine optimization (SEO) experts
Lack of systematical studies leads to folkloresLack of systematical studies leads to folklores
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality44
Various Ranking Feature OpinionsSEO expertsSEO experts Survey of
Internet usersSurvey of
Internet usersIndividual Internet marketing expert
Individual Internet marketing expert
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Goals & Challenges
● Goals■ Systematically approximate a search engine’s ranking
results■ Identify the importance of ranking factors
● Reverse-engineering a search engines’ ranking algorithms can be very complicated■ Numerous ranking factors
− Google claims to have over 200 ranking factors
■ Sophisticated ranking functions
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality66
Our Approach
● Build our own ranking system to approximate search engines’ ranking results
Learning models:• Linear programming • SVM
Recursive partitioning algorithm:• Capture non-equational behavior of ranking functions.
New ranking system:Generate our own ranking results and compare to Google’s
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality77
System Architecture
● Components of our ranking system■ Crawler■ Ranking Engine
Can we approximate Google’s ranking results (top 10 pages) by using our own ranking system?
Can we approximate Google’s ranking results (top 10 pages) by using our own ranking system?
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality88
Ranking Features
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality99
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Learning Models
● Linear programming model■ Minimize the distance between our ranking system and
Google’s■ Minimize objective function
● Support vector machine (SVM) learning models■ General technique for learning to rank programs■ Support linear and polynomial kernels
Weight: highly ranked pages are more important
Weight: highly ranked pages are more important
Ranking difference between the 2 pages
Ranking difference between the 2 pagesDecision function:
Out of order => penalty Decision function:
Out of order => penalty
Sum up the penaltiesSum up the penalties
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Recursive Partitioning Algorithm
● Multiple layers of indices● Non-equational ranking algorithm
While we need to partition the set of |S| pages
While we need to partition the set of |S| pagesPartition the |S| pages into top half and bottom half
Partition the |S| pages into top half and bottom halfReturn top half of the |S| pages
and continue the recursionReturn top half of the |S| pages
and continue the recursion
The algorithm ends when we found top X pages
The algorithm ends when we found top X pages
Train or apply ranking models to the set of |S| pages
Train or apply ranking models to the set of |S| pages
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality1111
Experimental Evaluation
● Evaluate different ranking models■ Which model has better prediction accuracy?
● Evaluate the effectiveness of recursive partitioning algorithm■ Can recursive partitioning algorithm improve prediction
accuracy?
● Evaluate the relative weights of ranking features■ Which ranking feature is more important?
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality1212
Experimental Setup
● Crawl top 100 pages of 60 random keywords
● Randomly select 15 keywords as the training set with the rest 45 keywords as the testing set
● Evaluate the accuracy of our ranking system by predicting Google’s top 10 pages for each keyword in the testing set
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality1313
Comparisons of Ranking Models
The performance of our customized linear learning is better than SVM-linear modelThe performance of our customized linear learning is better than SVM-linear model
The performance of the polynomial model is better than both linear models.At the cost of: (1)Significant increase of learning time(2)No human readable equations
The performance of the polynomial model is better than both linear models.At the cost of: (1)Significant increase of learning time(2)No human readable equations
For 78% of the explored keywords, our ranking system successfully predicts 7 or more pages within the top 10 pagesFor 78% of the explored keywords, our ranking system successfully predicts 7 or more pages within the top 10 pages
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality1414
The Power of Recursive Partitioning
The recursive partitioning algorithm does help to improve accuracy of the ranking system in every roundThe recursive partitioning algorithm does help to improve accuracy of the ranking system in every round
3 rounds of recursive partitioning successfully “smooth out” the non-linearity of Google ranking algorithm and achieve a high prediction accuracy
3 rounds of recursive partitioning successfully “smooth out” the non-linearity of Google ranking algorithm and achieve a high prediction accuracy
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality1515
Weights in Different Rounds in a Linear Model
In different rounds, the learning model produces different set of weightsIn different rounds, the learning model produces different set of weights
Page rank score, keyword in title and hostname are the top 3 ranking feature
Page rank score, keyword in title and hostname are the top 3 ranking feature
Keyword in meta-description tag matters but in meta-keyword tag does not
Keyword in meta-description tag matters but in meta-keyword tag does not
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality1616
Case Studies
● Can we improve our ranking system’s accuracy by isolating a subset of ranking features■ Example: remove the age factor by focusing on “young”
pages
● Can we use our ranking system to detect biases in search engines’ ranking algorithms?■ Example: blogs
● Can we validate or disapprove new ranking features?■ Example: HTML syntax errors
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality1717
Isolating Subsets of Ranking Features
We crawl web pages less or equal to 24 hours old to remove ranking features of age and page rank
We crawl web pages less or equal to 24 hours old to remove ranking features of age and page rankOur ranking system’s hit rate
improves to 80% for 92% of evaluated keywords
Our ranking system’s hit rate improves to 80% for 92% of evaluated keywords
When the ranking features are more specific, our ranking system performs betterWhen the ranking features are more specific, our ranking system performs better
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Negative Bias Toward Blogs
We categorized web pages to different categories (e.g. blogs, news and music) and add a new ranking feature (hypothesis) into our ranking system
We categorized web pages to different categories (e.g. blogs, news and music) and add a new ranking feature (hypothesis) into our ranking system
The accuracy of our ranking system improves and the weight of the new ranking feature (blog) is negative
The accuracy of our ranking system improves and the weight of the new ranking feature (blog) is negative
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HTML Syntax Errors do not Matter
We add a new ranking feature (hypothesis) for the number of HTML syntax errors in each web page
We add a new ranking feature (hypothesis) for the number of HTML syntax errors in each web page
The performance of the new ranking model is very close to the original one -> the new ranking feature does not make an impact
The performance of the new ranking model is very close to the original one -> the new ranking feature does not make an impact
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality2020
Conclusions
● In this work, we show that it is possible to systematically approximate Google’s ranking results with high accuracy■ By a linear learning model incorporated with a recursive
partitioning scheme
● We reveal the relative importance of ranking features in Google’s ranking function
● We illustrate our system can validate or disapprove ranking features and detect ranking bias
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Thank you!
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Backup Slides
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality
Linear Programming Model
Ao-Jan Su How to Improve Your Google Ranking: Myths and Reality
Query Keywords