A data driven approach to measure web site navigability

A Data-Driven Approach to Measure Web Site Navigability

Speaker ： ScottDate ： 6/13/14 (Fri)

Xiao FangPaul Jen-Hwa Hu

Michael ChauHan-fen Hu

Zhuo YangOlivia R. Liu Sheng

Journal of Management Information Systems

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

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

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.

Method and Metrics for Measuring Navigability

A Web Mining–Based Method for Measuring

Navigability

Steps1. Web log preprocessing ： Cleaning, session identification, session

completion.2. Web site parsing ： Parsing focal sites3. Web page classification ： Content pages and index pages4. Access pattern mining ： Frequently accessed sequences of

content pages as proxies for information-seeking targets5. Hyperlink Structure representation ： A distance matrix

Method and Metrics for Measuring Navigability

Data-Driven Metrics for Measuring Navigability

Method and Metrics for Measuring Navigability

Data-Driven Metrics for Measuring Navigability

Power

• , where is the jth content page in ,

• if , otherwise

Method and Metrics for Measuring Navigability

Data-Driven Metrics for Measuring Navigability

Power

• Introducing weight

Method and Metrics for Measuring Navigability

Data-Driven Metrics for Measuring Navigability

Efficiency

• , if

Method and Metrics for Measuring Navigability

Data-Driven Metrics for Measuring Navigability

Directness• if

• if

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

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%.

•

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

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.

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

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

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

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

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

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.

The End