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A Data-Driven Approach
to Measure Web Site
Navigability
Speaker:Scott
Date:6/13/14 (Fri)
Xiao Fang
Paul Jen-Hwa Hu
Michael...
Introduction
• A well-designed website is beneficial to visitors.
• Navigation and search
• Structure of hyperlinks
• Defi...
Literature Review
• Website navigation and navigability
 Critical influence of navigation
 Navigation systems, important...
Theoretical Foundations
• Information foraging theory
 It extends the optimal foraging theory
 Very likely to modify bro...
Method and Metrics for
Measuring Navigability
A Web Mining–Based
Method for Measuring
Navigability
Steps
1. Web log prepro...
Method and Metrics for
Measuring Navigability
Data-Driven Metrics for
Measuring Navigability
𝑝 𝑘 =
𝛽
2𝜋𝑘3 exp
−𝛽 𝑘−𝛼 2
2𝛼2...
Method and Metrics for
Measuring Navigability
Data-Driven Metrics for
Measuring Navigability
Power
• 𝑈 = 𝑢𝑖 , 𝑖 = 1,2, … ,...
Method and Metrics for
Measuring Navigability
Data-Driven Metrics for
Measuring Navigability
Power
• 𝑅 𝑢𝑖 = ∀𝑝 𝑆
𝑃(start o...
Method and Metrics for
Measuring Navigability
Data-Driven Metrics for
Measuring Navigability
Efficiency
• 𝑄 𝑢𝑖|𝑝𝑠 =
𝑚𝛾−min...
Method and Metrics for
Measuring Navigability
Data-Driven Metrics for
Measuring Navigability
Directness
• 𝐿 𝑢𝑖|𝑝𝑠 =
𝑚𝛿−min...
Implementation and Illustrations
• An archetype system was established.
• SpidersRUs was used to parse a website.
• Two si...
Implementation and Illustrations
•
• The threshold was at first set at 0.05%, then its value was increased
with 0.025% in ...
Implementation and Illustrations
• The distances of power and efficiency on B is great on A.
• The directness distances be...
Evaluation Study and Data Collection
Study design
• A group of people were recruited.
• The significance of users’ familia...
Evaluation Study and Data Collection
Participants
• Business undergraduate students enrolled in similar information
system...
Data Analyses and Results
• A pilot study, 39 undergraduate students
• An evaluation study with 248 participants
• Compari...
Extensions to Proposed Metrics
• A scale factor can be added while evaluating a larger website.
• The metrics can be exten...
Discussion
• Three data-driven metrics and a viable method were presented.
• The method can be used continuously for super...
Conclusion
• Three data-driven metrics were presented.
• By integrating appropriate Web mining techniques, a method
cooper...
Comment
• The article clearly and laconically expresses the idea and concept with
the existing theories.
• Vivid examples ...
The End
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A data driven approach to measure web site navigability

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A data driven approach to measure web site navigability

  1. 1. A Data-Driven Approach to Measure Web Site Navigability Speaker:Scott Date:6/13/14 (Fri) Xiao Fang Paul Jen-Hwa Hu Michael Chau Han-fen Hu Zhuo Yang Olivia R. Liu Sheng Journal of Management Information Systems
  2. 2. Introduction • A well-designed website is beneficial to visitors. • Navigation and search • Structure of hyperlinks • Definition of website navigability • Aside from perceptual measurements, a data-driven approach is also can be utilized to evaluate navigability of websites • Limitations of navigability processed by other scholars in the past • The objectives of the paper • Three metrics : power, efficiency, directness
  3. 3. Literature Review • Website navigation and navigability  Critical influence of navigation  Navigation systems, important means  Nuance between navigation and navigability • Measuring navigability with web data  Broad classification  Web content mining  Web structure mining  Web usage mining
  4. 4. Theoretical Foundations • Information foraging theory  It extends the optimal foraging theory  Very likely to modify browsing strategies • Information-processing theory  People process information via many aspects • Visitors make judgments about their traversing paths • What they care doesn’t merely contain the likelihood of locating target information.
  5. 5. Method and Metrics for Measuring Navigability A Web Mining–Based Method for Measuring Navigability Steps 1. Web log preprocessing:Cleaning, session identification, session completion. 2. Web site parsing:Parsing focal sites 3. Web page classification:Content pages and index pages 4. Access pattern mining:Frequently accessed sequences of content pages as proxies for information-seeking targets 5. Hyperlink Structure representation:A distance matrix
  6. 6. Method and Metrics for Measuring Navigability Data-Driven Metrics for Measuring Navigability 𝑝 𝑘 = 𝛽 2𝜋𝑘3 exp −𝛽 𝑘−𝛼 2 2𝛼2 𝑘 𝑘 = 1,2, … , 𝐺 𝐼 = ∀𝑘≥𝐼 𝑝(𝑘) 𝐼 = 1,2, … 𝐺 𝐼 = 1 if 𝐼 = 1 G 𝐼 − 1 − 𝑝 𝐼 − 1 if𝐼 > 1
  7. 7. Method and Metrics for Measuring Navigability Data-Driven Metrics for Measuring Navigability Power • 𝑈 = 𝑢𝑖 , 𝑖 = 1,2, … , 𝑛 • 𝑢𝑖 =< 𝑝𝑖,1, 𝑝𝑖,2, … , 𝑝𝑖,𝑚 >, where 𝑝𝑖,𝑗 is the jth content page in 𝑢𝑖, 𝑗 = 1,2, … , 𝑚 • 𝑅 𝑢𝑖|𝑝𝑠 = 𝐺 𝑑 𝑝𝑠, 𝑝𝑖,1 𝑗=2 𝑚 𝐺 𝑑 𝑝𝑖,𝑗−1, 𝑝𝑖,𝑗 if 𝑝𝑠 ≠ 𝑝𝑖,1, otherwise 𝑅 𝑢𝑖|𝑝𝑠 = 𝑗=2 𝑚 𝐺 𝑑 𝑝𝑖,𝑗−1, 𝑝𝑖,𝑗
  8. 8. Method and Metrics for Measuring Navigability Data-Driven Metrics for Measuring Navigability Power • 𝑅 𝑢𝑖 = ∀𝑝 𝑆 𝑃(start of seeking for 𝑢𝑖 = 𝑝𝑠)𝑅 𝑢𝑖|𝑝𝑠 • Introducing weight  𝑤 𝑢𝑖 = 𝑣(𝑢 𝑖) ∀𝑢∈𝑈 𝑣(𝑢)  𝑅 𝑈 = 𝑖=1 𝑛 𝑤 𝑢𝑖 𝑅(𝑢𝑖)
  9. 9. Method and Metrics for Measuring Navigability Data-Driven Metrics for Measuring Navigability Efficiency • 𝑄 𝑢𝑖|𝑝𝑠 = 𝑚𝛾−min(𝑑 𝑝 𝑠, 𝑝 𝑖,1 + 𝑗=2 𝑚 𝑑 𝑝 𝑖,𝑗−1, 𝑝 𝑖,𝑗 , 𝑚𝛾) 𝑚(𝛾−1) • 𝑄 𝑢𝑖|𝑝𝑠 = (𝑚−1)𝛾−min( 𝑗=2 𝑚 𝑑 𝑝 𝑖,𝑗−1, 𝑝 𝑖,𝑗 , (𝑚−1)𝛾) 𝑚(𝛾−1) , if 𝑝𝑠 = 𝑝𝑖,1 • 𝑄 𝑢𝑖 = ∀𝑝 𝑠 𝑃(start of seeking for 𝑢𝑖 = 𝑝𝑠) 𝑄 𝑢𝑖|𝑝𝑠 • 𝑄 𝑈 = 𝑖=1 𝑛 𝑤 𝑢𝑖 𝑄(𝑢𝑖)
  10. 10. Method and Metrics for Measuring Navigability Data-Driven Metrics for Measuring Navigability Directness • 𝐿 𝑢𝑖|𝑝𝑠 = 𝑚𝛿−min(𝑁 𝑝 𝑠,𝑝 𝑖,1 + 𝑗=2 𝑚 𝑑 𝑝 𝑖,𝑗−1, 𝑝 𝑖,𝑗 , 𝑚𝛿) 𝑚(𝛿−1) if 𝑝𝑠 ≠ 𝑝𝑖,1 • 𝐿 𝑢𝑖|𝑝𝑠 = (𝑚−1)𝛿−min( 𝑗=2 𝑚 𝑑 𝑝 𝑖,𝑗−1, 𝑝 𝑖,𝑗 , (𝑚−1)𝛿) (𝑚−1)(𝛿−1) if 𝑝𝑠 = 𝑝𝑖,1 • 𝐿 𝑢𝑖 = ∀𝑝 𝑠 𝑃(start of seeking for 𝑢𝑖 = 𝑝𝑠) 𝐿 𝑢𝑖|𝑝𝑠 • 𝐿 𝑈 = 𝑖=1 𝑛 𝑤 𝑢𝑖 𝐿(𝑢𝑖)
  11. 11. Implementation and Illustrations • An archetype system was established. • SpidersRUs was used to parse a website. • Two sites  A  3840 content pages  437 index pages • Web logs were gleaned over four weeks.  A:35,966,494 records; 732,321 sessions  B:32,170,062 records; 555,299 sessions  B  3738 content pages  380 index pages
  12. 12. Implementation and Illustrations • • The threshold was at first set at 0.05%, then its value was increased with 0.025% in the range from 0.05% to 0.175%. •
  13. 13. Implementation and Illustrations • The distances of power and efficiency on B is great on A. • The directness distances between A and B are smaller than that of power and efficiency. • According to the proposed metrics, A has higher navigability than B • The assessment of the proposed metrics and the prevalent metrics
  14. 14. Evaluation Study and Data Collection Study design • A group of people were recruited. • The significance of users’ familiarity was addressed. • Four experimental conditions were created Tasks • A pretest was conducted.  Content pages are more likely to constitute information-seeking targets.  Key access sequences identified from Web logs are consistent with users’ common information-seeking needs, desires, and interests.
  15. 15. Evaluation Study and Data Collection Participants • Business undergraduate students enrolled in similar information systems or operations classes in both universities. • Each participant received $10 for his or her time and efforts. Measurements • Three measures: task success rate, task time, and the number of clicks. • Participants had up to 4 minutes to complete each task. • Cognitive-processing load Data collection • A quite formal way
  16. 16. Data Analyses and Results • A pilot study, 39 undergraduate students • An evaluation study with 248 participants • Comparison of user performance and assessments between A and B • Comparison of user performance by separating tasks related to complexity • Performance of the participants from each university • An ex post facto comparison • Further examination of the proposed metrics
  17. 17. Extensions to Proposed Metrics • A scale factor can be added while evaluating a larger website. • The metrics can be extended with the combined use of search engine. • Integration of three metrics as a holistic measure 3 1 𝑅(𝑈) + 1 Q(𝑈) + 1 𝐿(𝑈) = 3𝑅 𝑈 Q 𝑈 𝐿 𝑈 Q 𝑈 𝐿 𝑈 + 𝑅 𝑈 𝐿 𝑈 + 𝑅 𝑈 Q 𝑈 = 𝑂(𝑈)
  18. 18. Discussion • Three data-driven metrics and a viable method were presented. • The method can be used continuously for supervising a website’s navigability • A method by Liu et al. is suggested for gleaning data (Web log). • It helps improve hyperlink structure designs of websites • Limitations • Different structures of websites may not fit to the results • Spiders and page parsers‘ utilities are limited. • Test of different scenarios • More factors can be introduced to perfect the method
  19. 19. Conclusion • Three data-driven metrics were presented. • By integrating appropriate Web mining techniques, a method cooperated the metrics was created. • The verification of the metrics and method. • Users’ perception corresponds to navigability measured using the methods established by the authors
  20. 20. Comment • The article clearly and laconically expresses the idea and concept with the existing theories. • Vivid examples following many statements which we as post-graduate students can look upon. • A host of demonstrations below on many pages provide necessary assistance for lay people • I think navigability won’t be only one factor that may affect a website access ratio.
  21. 21. The End

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