DOI: 10.1109/CSNT.2015.15 Conference: 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT)

1. 1
An Efficient Approach for Improving
Website Design
Presented
by
Hemant Kumar Singh

2. Introduction
Primary cause of poor website design is difference in understanding of
developers view and the user’s view & web developers may not have a
prediction of users’ Interests at time of creation of site
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3. Two approaches to improve the web user navigation-
 Web personalization approach: Web personalization is the process of
“tailoring” WebPages to the needs of specific users using the information of
the users’ navigational behavior and profile data
3

4.  Web Transformation approach: involves changing the structure of a website
to smooth the progress of the browsing for a large set of users instead of
personalizing pages for individual users
 The threshold value may be decided on the basis of click ratio of previous
access log of a user for a particular website
 Click ratio is average number of clicks required to reach the destination
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5. Proposed Algorithm
Proposed algorithm for finding the click ratio which informs the webmasters
that their website is not meeting user’s behavior and needs, it mean this
website needs to be restructured. Therefore proposed efficient approach is
suitable for maintenance of the complex websites and can be applied in
regular manner after a particular time period
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6. Steps
• Step1: Minimize
• Here c(s)=> smallest number of clicks needed to traverse the node of s in
sequence
• N(S) is the number of distractive pages in S
• f(s) is frequency of occurrence of s
• the constraints are that
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7. • step 1(a): when traversing a bundle, we can only move along edges that are
present in the graph.
• Step 1(b): there can be at most d(i) links in any page i.
• Step 1(c): for each bundle S, the variables set to 1 must define a path that
passes through the documents contained in bundle S in the proper sequence.
Such a defined path could contain documents that do not belong to S.
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8. Step 1(d): total number of edges in the traversal path, which in
this case equals the total number of mouse clicks, can be computed
from the value. Once c(s) is known for each bundle S, Q(G) can
determined directly.
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9. AMPL code
• set T;
• set Pos;
• set docno {Pos, T} within V;
• var X {i in V, j in V} binary;
• var Y {i in V, j in V, s in T, p in Pos} binary;
• param d {V};
• param L {T};
• param W {T};
• minimize Optimized_click_ratio :
• sum {s in T} W[s]*(1 + sum {i in V, j in V, p in 1..L[s]-1} Y[i, j, s, p]);
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10. • subject to step 1a {i in V, j in V, s in T, p in 1..L[s]-1}:
• Y[i, j, s, p] <= X[i, j];
• subject to step1b {i in V}:
• sum {j in V} X[i, j]<= d[i];
• subject to step 1c {i in V, s in T, p in 1..L[s]-1, h in docno [p,s], k in docno [p+1,s]}:
• sum {j in V} Y[i, j, s, p] - sum {j in V} Y[j, i, s, p] = if i == h then 1 else if i == k then -1 else if i != h && i != k
then 0;
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11. • set V := A, B, C, D, E, F, H, J;
• set T := s1, s2, s3, s4;
• set Pos := 1, 2, 3, 4, 5, 6, 7, 8, 9, 10;
• set docno[1, s1] := A;
• set docno[2, s1] := D;
• set docno[3, s1] := C;
• set docno[4, s1] := A;
• set docno[5, s1] := B;
• set docno[6, s1] := C;
• set docno[7, s1] := F;
• set docno[8, s1] := E11
set docno[1, s2] := C;
set docno[2, s2] := F;
set docno[3, s2] := E;
set docno[4, s2] := B;
set docno[5, s2] := C;
set docno[6, s2] := F;
set docno[7, s2] := E;
set docno[8, s2] := J;
set docno[9, s2] := H;
set docno[10, s2] := J;
set docno[1, s3] := D;
set docno[2, s3] := F;
set docno[3, s3] := E;
set docno[4, s3] := F;
set docno[5, s3] := D;
set docno[6, s3] := C;
set docno[7, s3] := A;
set docno[8, s3] := B;
set docno[1, s4] := J;
set docno[2, s4] := B;
set docno[3, s4] := J;
set docno[4, s4] := H;
set docno[5, s4] := E;
#W is f(S)/N(S)
Data file

12. • V -A set V of N nodes, each corresponding to a page
• S -Is a Bundle. Bundle can be viewed as set of pages ordered by time spent on
page.
• T- Ordered sequences (bundles) of nodes with the lengths of ordered sequence
and the number of times this ordered sequence occurs
• L(S)-Length of bundle S counting duplications
• N(S)-Number of distinct documents in S
• f(S)-Frequency of occurrence of S
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13. • N-Set of all web pages
• d(i)-Maximum number of links on a page
• Rd-Target traversability ratio A typical user traversal i.e. number of transient
pages per content page
• Ru-User acceptability ratio i.e. number of transient pages tolerated per content
page
• Cfeas-Best feasible click ratio defined as C’/N(S) is minimum number of
mouse clicks needed to traverse all the pages in the bundle in the specified
order. N(S) is the number of distinct content pages in a given bundle S.
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14. • Corig-Original click ratio defined as c/p is the click ratio of the session. Where
c is total mouse clicks and p is the total district pages in the bundle.
• Copt-Link-optimized click ratio
• Q(G)-Measure of the optimized click ratio
• W(S)-Weight of bundle S
• Docno(pos, S)-Document at position pos in bundle S
• X, Y-Decision Variables
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15. Example 1:
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Figure: Website of 10 pages
Start page
Web Page Backtrack page
Link between Pages
Traversal Path
I
E
H
B K
F
J
D C
A

16. Figure: Website of 8 pages
16
F
Start page
H
J
D
E
B
C
A
Web Page Backtrack page
Link between Pages
Traversal Path

17. Figure : one possible traversal
17
H
J
D
E
B
F
C
A
Start page

18. Figure : another possible traversal
18
F
Start page
H
J
D
E
B
C
A

19. Figure : best possible traversal
19
F
Start page
H
J
D
E
B
C
A

20. Experimental Evaluation
• AAMPL code was written for this website in hem.mod file the input data is
in LINKOPT_NB.dat and solved using MINOS 5.51 and CPLEX solvers [9]
on Pentium ® dual core CPU E5400@ 2.70 GHz with 2GB RAM and 32 bit
windows operating system.
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sw: ampl
ampl: model hem.mod;
ampl: data LINKOPT_NB.dat;
ampl: solve;
MINOS 5.51: ignoring integrality of 1792
variables
MINOS 5.51: optimal solution found.
8085 iterations, objective 1.25
ampl:

21. Conclusion and Future directions
 We have proposed an efficient approach which is based on the threshold value.
It is stated that if the Average click ratio is greater that threshold (eg.
threshold=2) it means web personalization or web transformation is required
i.e. either improve the linkage on web page or reshuffle the web pages.
 The proposed analytical approach is coded in a mathematical programming
language known as AMPL with CPLEX and MINOS 5.5 solver to find the
average number of clicks required to reach the destination.
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22.  The proposed efficient approach can be used to solve the problem that when
the re-linking or rearrangement of website is required. Re-linking will take
place only when necessary
 In future the work can be extended to create a new link optimization problem
and can be solved how many links should be increased on particular page and
cost benefit analysis can be done that is it better to change the structure of
website or improve the linkage on web pages.
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23. References
[1] Min Chen, Young U. Ryu, "Facilitating Effective User Navigation through Website Structure Improvement," IEEE
Transactions on Knowledge and Data Engineering, vol. 25, no. 3, pp. 571-588, March 2013, doi:10.1109/TKDE.2011.238
[2] J. Palmer, “Web Site Usability, Design, and Performance Metrics,”Information Systems Research, vol. 13, no. 2, pp. 151-167,
2002.
[3] V. McKinney, K. Yoon, and F. Zahedi, “The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation
Approach,” Information Systems Research, vol. 13, no. 3, pp. 296- 315, 2002.
[4] M. Kilfoil et al., “Toward an Adaptive Web: The State of the Art and Science,” Proc. Comm. Network and Services Research Conf.,
pp. 119-130, 2003
[5] R. Gupta, A. Bagchi, and S. Sarkar, “Improving Linkage of Web Pages,” INFORMS J. Computing, vol. 19, no. 1, pp. 127-136,
2007
[6] M. Eirinaki and M. Vazirgiannis, “Web Mining for Web Personalization,” ACM Trans. Internet Technology, vol. 3, no. 1,pp. 1-27,
2003
[7] B. Mobasher, “Data Mining for Personalization,” The Adaptive Web: Methods and Strategies of Web Personalization, A. Kobsa, W.
Nejdl, P. Brusilovsky, eds., vol. 4321, pp. 90-135, Springer-Verlag,2007.
[8] C.C. Lin and L. Tseng, “Website Reorganization Using an Ant Colony System,” Expert Systems with Applications, vol. 37, no. 12,
pp. 7598-7605, 2010.
[9] http://ampl.com/resources/the-ampl-book/
[10]http://ampl.com/try-ampl/get-a-trial-license/
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THANK YOU
END