Li, P. and Yamada, S.

Paper:

Extraction of Web Site Evaluation Criteria andAutomatic Evaluation
Peng Li∗ and Seiji Yamada∗∗∗ Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology

J2, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
E-mail:liorlee@nii.ac.jp∗∗ National Institute of Informatics, SOKENDAI
2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan[Received December 11, 2009; accepted March 8, 2010]

This paper proposes an automated web site evaluation
using machine learning to extract evaluation criteria
from existing evaluation data. Web site evaluation is asignificant task because evaluated web sites provide information useful to users in estimating sites validationand popularity. Although many practical approaches
have been taken to present possible measuring sticks
for web sites, their evaluation criteria are manually
determined. We developed a method to obtain evaluation criteria automatically and rank web sites withthe learned classifier. Evaluation criteria are discriminant functions learned from a set of ranking information and evaluation features collected automatically by
web robots. Experiments confirmed the effectivenessof our approach and its potential in high-quality website evaluation.

Keywords: evaluation criteria extraction, web site evaluation

1. Introduction
Internet users number over 1.7 billion [1] and the number of web sites has grown exponentially. A November 2009 survey estimated 233 million [2] of these rich,highly updated information sources. Publishing on theInternet is easy, cheap, and fast. However, the web’s
very nature means that not all information is adequately
or clearly attributed, or complete and trustworthy.
This had increased the use of web site evaluations infields such as business, universities, government, and Ecommerce. Based on Jupiter Research Reports, budget
earmarked for web site evaluation products and services
exceeded 1 billion US dollars annually by 2006.
No unified criteria currently exist that are fully trustworthy for web site evaluations. Each user has different
criteria, and it is almost impossible to get a positive result using the same evaluation criteria on web sites of different categories. Moreover, the evaluation changes overtime even for the same web site. Although many tools forevaluating the usability, accessibility, and security of web

sites [3–6] have started to appear, the evaluation tasks arebasically still done manually [7]. There are limitations as
to how many web sites can be manually evaluated, and an
automated evaluation system has been seen as a necessary
step.
In this paper, we propose an automated method to extract evaluation criteria. This involves focusing on categorized web sites and setting evaluation criteria for individual categories applying machine learning to deal withranking. We then use web robots to collect the features ofthe evaluation objectives and evaluate them automatically
based on their features and the criterion of the corresponding category.
Establishing evaluation criteria requires that evaluation data be obtained regularly and automatically fromthe general public – a task conventionally done by questionnaires. This has changed however, with the adventof registration ranking sites – real-time web site ranking services based on user recommendations and votes.
The most popular of these, such as SiteRank1 , have hundreds of thousands of web sites registered and users makecategories, register new sites, vote, and write comments
frequently. In this research, we use categorized ranking information from registration ranking sites to set up
web sites evaluation criteria. In other words, we treat
automated web site evaluation as a classification learning problem in which discriminant functions are learned
by categorized ranking information used as training data.
We compared three major ranking algorithm – Ranking
SVM [8], Prank [9], and Multiclass SVM [10] – to findthe one best adapted to solving the ranking problem.

2. BackgroundWeb site evaluation task requires appropriate evaluation measures. Researches such as Web WISDOM [11]and WebMac [12] use indexes from library and information science findings as meta-information to create evaluation measures, although these do not particularly target
automation.
Aside from the approach of the questionnaire sur1. http://siterank.org/jp/

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Extraction of Web Site Evaluation Criteria

1 < i < K, define x̄+j and x̄−j as n + K − 2 dimensional
vectors as follows: x j [l] 1 ≤ l ≤ n
0n < l < n+i−1
x̄+j [l] = 1
n+i−1 ≤ l ≤ n+K −2

Fig. 1. Ranking.

;vey, Velayathan and Yamada [13] proposed automaticallylearning evaluation criteria based on user browsing behavior, but evaluation objectives were web pages, not web
sites, and users had to browse evaluated objectives to ascertain browsing behavior.
Tools focusing on evaluating web site usability, accessibility, and security are roughly divided into (1) server performance analysis, (2) usage analysis based on log data,(3) guideline compliance checking, (4) navigation text
analysis, (5) cyber agent navigation simulation [14]. Automatically evaluating web sites by these tools, however,involves evaluation criteria predetermined by experts.

3. Evaluation Criteria Extraction
We define evaluation criteria as discriminant functions
learned from a set of ranking information. In this section,
we introduce algorithms that solve the ranking problem
and detail evaluation features and procedures.

3.1. Ranking Algorithms
Our ranking problem uses ranking information as both
input and output. Algorithms such as Ranking SVM,
Prank, and Multiclass SVM used to solve this kind ofproblem are detailed below.
3.1.1. Ranking SVM
As presented graphically in Fig. 1, the distancefrom hyperplane w of datapoint x is mapped onto onedimensional (1D) space in which θ1 , . . . , θK−1 are different thresholds against which distance is compared. Data
point x having rank i satisfies θi−1 < wT x j < θi . Checking
which section wT x j goes into helps determine the rank of
x. Define αi = θi+1 − θi ; 1 ≤ i ≤ K − 1. No lower boundexists for data items belonging to rank 1 and all data items
belonging to rank K have no upper bound. Note that data
point x j having rank i > 1 satisfies the following:K−2

wT x j > θi−1 ; wT x j + αi−1 > θi ; wT x j + ∑ αkk=i−1

> θK−1 .

. . . . . . . . . . . . (1)Similarly, for an example with rank i < K, we have thefollowing:K−2

wT x j < θi ; wT x j + αi < θi+1 ; wT x j + ∑ αkk=i

< θK−1 .

. . . . . . . . . . . . (2)Based on these observations,define w̄ =[w, α1 , α2 , . . . , αK−2 ] and for example x j with rank
Vol.14 No.4, 2010
 x j [l] 1 ≤ l ≤ n
0n < l < n+ix̄−j [l] = 1n+i ≤ l ≤ n+K −2. . (3)This leads to the following:w̄T x̄+j > 0 ⇒ wT x j > θi−1 ; w̄T x̄−j < 0⇒ wT x j < θi .

. . . . . . . . (4)Once x̄+j and x̄−j are defined, the ranking problem is simply reduced to learning classifier w̄ in n + K − 2 dimensional space so that w̄T x̄+j > 0 and w̄T x̄−j < 0. This standard classification problem has at most 2m training examples, half of which have label +1 (examples x̄+j ) and theother half label −1 (examples x̄−j ).
Many real-world problems may not yield a linear function powerful enough to learn data ranking, so nonlinear
mapping φ () is used over the original feature vector. If x j
has rank i, then Eq. (4) is satisfied. However:κ (w̄, x̄+j ) = φ (w̄)T φ (x̄+j ) > 0 ⇒ φ (w)T φ (x j )> θi−1 . . . . . . . . . . . .

(5)

is not satisfied due to the nonlinearity of φ (). To getaround this problem, a new kernel function has to be defined. For kernel function K and corresponding mapping
φ , define new kernel function κ̄ with the corresponding
mapping φ () as follows:φ (w̄) = [φ (w), w̄[n + 1 : n + K − 2]];

φ (x̄) = [φ (x), x̄[n + 1 : n + K − 2]].

. . . . (6)A kernel trick becomes available with the new kernel
function:κ̄ (w̄, x̄) = κ (w, x). + w̄ [n + 1 : n + K − 2]x̄ [n + 1 : n + K − 2]

. (7)3.1.2. Prank
Prank, a perceptron-based online learning algorithm,
trains a perceptron model, retaining both weight vector
w and threshold vector b, with the objective of finding aperceptron model that projects all instances into k subintervals defined by b. Fig. 2 shows the Prank algorithm.
3.1.3. Multiclass SVM
Multiclass SVM, a generalization of two-class SVM
systems, deals with multiclass problems mainly by breaking down single multiclass problems into multiple binary
problems, each yielding a binary classifier assumed to
produce an output function giving relatively large values

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Li, P. and Yamada, S.

Initialize: Set w1 0,b11,...,bk1 1 0,bk1 .
Loop: For t = 1,2,…,Ttn.
Get a new rank-value x
t
t t
t
ˆ
Predict y minr {1,...k}{r : w x br 0}.
t
Get a new label y .
tt
If yˆ y update wt (otherwise set wt 1 wt , r : brt 1 brt ) :t
1. For r = 1,…,k – 1 : If y t r Then yr 1tElse yr 1.
tt
2. For r = 1,…,k – 1 : If (wt xt brt ) yrt 0Then r yrtElse r 0
3. Update wt 1 wt ( r rt )xt .
For r = 1,…,k – 1 update: brt 1 brt rt
Output: H(x) minr {1,...,k}{r : wT 1 x brT 1 0}.

Fig. 2. Prank algorithm.

to examples from the positive class and relatively smallvalues to examples belonging to the negative class. To
deal with k-class classification, for example, it generates
set of 2-class classification SVMs f 1 ,. . . , f k that discriminate between one class and the rest, then classifies multiclasses based on Eq. (8) to integrate f 1 ,. . . , f k .
arg max g j (x).
j=1,...k. . . . . . . . . . . . (8)g j (x) is expressed as follows:lg j (x) = ∑ yi αij · k(x, x j ) + b j .

. . . . . . (9)i=1

The final complex function is as follows:f j (x) = sgn(g j (x)).

. . . . . . . . . . (10)3.2. Evaluation Features
We use ranking information from registration ranking
sites as training data. Each data consists of multiple evaluation features and their values, and the data value isthe rank determined by ranking sites. Using discriminantfunctions learned from training data, we rank unranked
web sites based on their features. Data from these unranked web sites is test data.
Sinha et al. [15] and Ivory et al. [16] studied “best” websites selected by experts and users to identify common
features. These include content, structure and navigation,
visual design, functionality, and interactivity. Palmer [17]concluded from three consecutive tests that web site success depends on speed, navigation, content, interactivity,
and response. These researches deal with both subjective
and objective user features. Our goal, however, is to automate evaluation, so the features we need must be objective and obtained automatically from web servers. Table 1
lists 31 features categorized in 10 fields currently used inour research and detailed below.
(i) Global Link Popularity shows a web page’s popularity by examining the link structure of the web. We use
Google PageRank here.
(ii) Freshness is the contrast of new and old information.
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Table 1. Ten fields of evaluation features.
Field
Top page’s global link popularity
Freshness
Indexable text information
Multi-media content
Accuracy of spelling and grammar
Accuracy of HTML documents
Content security
Content constitution
DesignOthersThe number of features
1
2
1
4
1
4
5
4
5
4

We use update frequency and the amount of text information changed as a guide.
(iii) Indexable Text Information is the amount of visible
HTML text information obtained by calculating the sizeof HTML source files with tag information removed.
(iv) Multimedia Content means the number of files including images, videos, audios and flashes.
(v) Spelling and Grammar Accuracy counts the numberof misspellings.
(vi) HTML Document Accuracy checks features such aswhether character code and image size are specified.
(vii) Content Security checks features related to the control of cache, scripts, and web robots.
(viii) Content Constitution uses the proportion of multimedia content to text information as a guide.
(ix) Design considers information on BGM, backgroundimage, frames, style sheets, etc.
(x) Others includes the number of links on the top page,
the number of links overall, whether the page author is
specified, and whether the page description is specified.
These 31 features were selected under the restrictionof currently available web server technology. Althoughother features will become available as web technology
advances, we believe these features are appropriate for our
purpose.

3.3. Automated Web Site Evaluation
As shown in Fig. 3 overview of our web site evaluation system, in the learning phase, categorized ranking information from registration ranking sites and evaluation
features collected by web robots are input to the ranking
learning machine to learn categorized discriminant functions. In the test phase, ranks of unranked web sites are
predicted automatically based on site’s features and corresponding discriminant functions.

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Extraction of Web Site Evaluation Criteria

Table 2. Experimental algorithm comparison.
MAE
Ranking SVM
Multi-class SVM
Prank

0.78
0.93
1.12

Minimum number ofadjacent transpositions
132.7
217.4
264.5

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㻝㻘㻞㻌

Fig. 3. System overview.

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4. Experiments㻜㻘㻞㻌

We compared ranking algorithms Ranking SVM,
Prank, and Multiclass SVM experimentally to find theone best adapted to this research. We then verified theadequacy of extracted evaluation criteria and studied thecorrelation between evaluation features and classification
results and compared our evaluation to a web site importance estimation approach.
The dataset consisted of 735 web sites in seven categories in which each category had web sites divided into
five equal groups and had labels assigned ranging from 1,
Excellent, to 5, Not expected. The 31 evaluation features
discussed in Section 3.2 were selected. LibSVM2.84 wasused as the SVM tool.

4.1. Evaluation Measures
We used Mean Absolute Error (MAE) – a statisticalquantity used to measure how close forecasts or predictions are to eventual outcomes – to evaluate performance.
MAE is given by:

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Category㻲㻳Fig. 4. Evaluation experiment results. (The y-axis represents the values of MAE, and the x-axis corresponds to categories A: computer science, B: music, C: health and diet,
D: movies, E: sports, F: games, and G: fashion.)

4.3. Algorithm Comparison
We experimentally compared Ranking SVM, Prank,
and Multiclass SVM, getting the results shown in Table 2.
To get detailed information in addition to MAE, we evaluated the three algorithms by calculating the minimum
number of adjacent transpositions [18] needed to bring thepredicted value to ground truth. As Table 2 shows, Ranking SVM significantly outperformed Prank and Multiclass
SVM, implying that the ranking problem in this research
is nonlinear.

4.2. Data Preprocessing and Kernel Selection

4.4. Criteria Extraction Accuracy Evaluation
In an experiment to verify the adequacy of extracted
evaluation criteria, we conducted 10-fold cross-validationin each category, as shown in Fig. 4, to minimize the influence of biased data. Average MAE is 0.78 and standard
deviation is 0.3, meaning that, in most cases, the predictedvalue is either equal to the true value or shifted one rank
from it. We looked at studies [19–21] that also use MAE
with a 5-level evaluation and found that keeping MAE under 1 is sufficiently effective.

Values of the evaluation features vary from 0 to millions, so we scaled data pre-experimentally to [0,1]. LibSVM provides four kernels – linear, polynomial, radialbasis function, and sigmoid. Experimentally studying kernel accuracy, we decided to use the RBF kernel due to its
superior accuracy and reprogrammed part of the kernel
source code to apply the kernel trick for Ranking SVM.
We did a grid search using cross-validation to find thebest value of RBF kernel parameters – c and γ – for eachof the seven web site categories.

4.5. Feature Contribution Ratio Experiment
It is important for users and web site authors to know
the correlation between evaluation features and classification results. Since the ranking problem in this research isnonlinear and we use a kernel trick in Ranking SVM, wecannot obtain the feature contribution ratio directly fromthe learned discriminant functions, so we used a wrapper [22], widely used in feature selection, to determine
the feature contribution ratio. Table 3 shows the top five

N

MAE =

∑ |pi − qi |i=1.
. . . . . . . . . (11)N
qi is prediction, pi the true value, and N the number of
items – the smaller the MAE, the higher the classification
accuracy.

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Li, P. and Yamada, S.

Table 3. Top five features by category.
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5

computer scienceIndexable text information
Percentage of updated text information
Number of links (site)
Specification of author information
Top page’s PageRank
music
Number of links (top page)
Number of audio files
Number of links (site)
Top page’s PageRank
Percentage of audio files
health and dietIndexable text information
Number of image files
PR for search robots
Specification of image size
Number of links (site)
moviesNumber of links (site)
Number of image files
Number of links (top page)
Percentage of image files
Specification of author information
sportsNumber of image files
Number of links (top page)
Indexable text information
Specification of background image
Top page’s PageRank
games
Number of links (site)
Number of image files
Specification of background image
Number of links (top page)
Indexable text information
fashionIndexable text information
Percentage of updated text information
Number of image files
Specification of cache expiration dateSpecification of font code

most contributed evaluation features in each category. We
concluded that feature contribution ratios vary with thecategory.

4.6. Comparison to Existing Importanc
Estimation
To verify its effectiveness, we compared our evaluation to an existing web site importance estimation approach. We chose PageRank, the most common importance estimation, for our comparison. Classification accuracy was compared for each category, as shown in Table 4. Our evaluation shows better classification accuracy
than PageRank in all categories.
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Table 4. MAE comparison to PageRank.
Category
Computer science
Music
Health and diet
MoviesSportsGames
FashionAverage MAE

Proposal
0.73
0.56
1.03
0.44
0.72
0.67
1.33
0.78

PageRank
1.57
2.12
1.86
1.73
2.23
2.57
1.48
1.94

5. Discussion
5.1. Category Influence
As shown in Fig. 4, differences in evaluation accuracy
between categories are great. Categories with low accuracy exist for two reasons:(1) It is difficult to set unified evaluation criteria forsome categories due to their characteristics, e.g., users
visiting health and diet web sites may have very different objectives – looking for health advice, food, recipes,
or even body-building equipments – from users visiting
movie and music category web sites, meaning that different purposes lead to different evaluation criteria.
(2) Important features that are the key to determining
evaluation criteria are not acquired experimentally, e.g., if
beautiful high-resolution images are preferred in the fashion category, it is difficult to set a high accuracy criterion without a feature describing it. More evaluation features are likely to become available with continuing improvement of web robots and web technology. With more
adapted features, things improve for the low-accuracy categories.
5.2. Training Data Quantity and Quality
Our data came from over 700 web sites. To get enoughfor each category, we collected data from multiple registration ranking sites, although this may potentially decrease evaluation accuracy due to changes in user groupand the web site quality bias. With further development ofregistration ranking sites, enough data will become available from a specific site, at which point evaluation accuracy is expected to improve. With registration ranking site
categories becoming more specific, we expect evaluation
accuracy to increase as categories become more specialized.
5.3. Evaluation Features and Contribution Ratio
Table 3 shows that features such as indexable text information and numbers of links play an important rolein most categories. However, the fact that some features
are effective only for corresponding categories shows thatfeatures reflect category characteristics. Features in Table 3, for example, contribute much to criteria extraction
accuracy, although some features hardly have any effector even an opposite effect – in which case it may be advantageous to remove such features at this time. Because

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Extraction of Web Site Evaluation Criteria

evaluation criteria change over time, however, and somefeatures may eventually become effective, we do not remove any existing features.

6. Conclusions
Improving the quality of web content is imperative forfurther web development, because good measuring sticks
are needed for web sites. Many practical approaches havebeen made in this regard, but have required manual intervention, preventing dealing with the entire web.
The automated web site evaluation we have proposed
involves defining web site evaluation as web site ranking and applying machine learning to extract evaluation
criteria. We compared three ranking algorithms, finding
Ranking SVM to be the best adapted to solving the ranking problem. Our evaluation experiments confirmed thefeasibility of extracted evaluation criteria. We also compared our evaluation to an existing web site importanceestimation approach and verified that our evaluation has
the advantage of evaluation accuracy. We also conductedfeature contribution experiments to determine the correlation between evaluation features and classification results.
We have presented a practical approach to automatically evaluating web sites that has high potential in highquality web site evaluation.

[15] R. Sinha, M. Hearst, and M. Ivory, “Content or Graphics?: An Empirical Analysis of Criteria for Award-Winning Websites,” Proc. ofthe 7th Conf. on Human Factors and the Web, 2001.
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Web Sites,” Proc. of the 20th ACM Conf. on Human Factors in
Computing Systems, 2002.
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[18] G. Lebanon and J. Lafferty, “Cranking: Combining rankings using
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the 10th Int. Conf. on World Wide Web, pp. 285-295, 2001.
[21] P. Li and S. Yamada, “A Movie Recommender System Based on
Inductive Learning,” Proc. of IEEE Conf. on Cybernetics and Intelligent Systems, Vol.1, pp. 318-323, 2004.
[22] R. Kohavi, and G. H. John, “Wrappers for Feature Subset Selection,” Artificial Intelligence, Vol.97, No.1-2, pp. 273-324, 1997.

Name:
Peng Li

Affiliation:
Tokyo Institute of Technology

Address:4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Brief Biographical History:

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2002- Master’s Student, Tokyo Institute of Technology
2004- Doctor’s Student, Tokyo Institute of Technology
2010- Researcher, Productive Leader Incubation Platform, Tokyo Instituteof Technology

Main Works:• “Web Site Evaluation Criteria Extraction and Evaluation Automation,”
IEEJ Japan, Vol.130, No.1, 66-74, Jan. 2010.

Membership in Academic Societies:• The Japanese Society for Artificial Intelligence (JSAI)

Name:
Seiji Yamada

Affiliation:
National Institute of Informatics, the graduate
university for advanced studies (SOKENDAI)

Address:
2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan

Brief Biographical History:
1989- Research Associate, Osaka University
1991- Lecturer, Osaka University
1996- Associate Professor, Tokyo Institute of Technology
2002- Professor, National Institute of Informatics, SOKENDAI

Main Works:• human-agent interaction, intelligent interactive systems, interactive
machine learning

Membership in Academic Societies:• The Institute of Electrical and Electronics Engineers, Inc. (IEEE)• Association for the Advancement of Artificial Intelligence (AAAI)• The Japanese Society for Artificial Intelligence (JSAI)

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