A Survey on Improving User Navigation through Website Structure Improvement

International Journal of Innovative Research in Advanced Engineering (IJIRAE)
Volume 1 Issue 2 (April 2014)
ISSN: 2278-2311 IJIRAE | http://ijirae.com
© 2014, IJIRAE – All Rights Reserved Page - 41
A Survey on Improving User Navigation through Website
Structure Improvement
S.R. Nagpurkar
Dept.of Comp.Engg.PVPIT, Pune
snagpurkar@gmail.com
Y.B.Gurav
Dept. of Comp.Engg.PVPIT, Pune
ybgurav@gmail.com
Abstract— Website navigation has been seemed as one of the most important design features across many domains,
including finance, e-commerce, entertainment, education, government, and medical A main reason is that the web
developers indulgent of how a website should be structured can be significantly different from that of the users. While
various method have been proposed to relink WebPages to improve navigability using user navigation data, the
completely reorganized new structure can be highly unpredictable, and the cost of disorienting users after the changes
remains unanalyzed .The start of the Internet has provided an extraordinary platform for people to gain knowledge
and explore information. How to efficiently and effectively retrieve required Web pages on the Web is becoming a
challenge.There are 1.73 billion Internet users worldwide as of September 2009, an increase of 18 percent since 2008.
The fast-growing number of Internet users also presents huge business opportunities to firms.
Keywords— Website design, user navigation, web mining, web navigation
I. INTRODUCTION
The two predominant paradigms for finding information on the Web are navigation and search [Olston and Chi 2003].
Most Web users typically use a Web browser to navigate a Web site. They start with the home page or a Web page found
through a search engine or linked from another Web site, and then follows the hyperlinks they think relevant in the
starting page and the subsequent pages, until they have found the desired information in one or more pages. They may
also use search facilities provided on the Web site to speed up information searching. For a Web site consisting of a very
large number of Web pages and hyperlinks between them, these methods are not sufficient for users to find the desired
information effectively and efficiently.
It is still an issue for users to find the desired information on the Web site effectively and efficiently. When users get
frustrated in their attempts to find the desired information, it only takes them one click to leave the Web site. Thus how to
help users navigate a Web site and find the desired information effectively and efficiently is crucial to the success of the
Web site. It is not always easy for users to find the desired information on the Web site effectively and efficiently. When
they get frustrated in their attempts to find the desired information, it only takes them one click to leave the Web site.
Thus how to help users navigate a Web site and find the desired information effectively and efficiently is crucial to the
success of the Web site.
A most important cause of poor website design is that the web developers’ perceptive of how a website should be
structured and can be considerably different from those of the users. Such differences result in cases where users cannot
easily find the desired information in a website. This issue is difficult to handle because when creating a website, web
developers may not have a clear understanding of users’ preferences and can only organize pages based on their own
ideas. However, the measure of website effectiveness should be the satisfaction of the users rather than that of the
developers. Thus, WebPages should be organized in a way that generally matches the user’s model of how pages should
be organized Therefore The problem of improving user navigation on a website with minimal changes to the current
structure, is an important issue.
Previous studies on website has focused on a variety of issues, such as understanding web structures [13], finding
relevant pages of a given page [14], mining informative structure of a news website [15], [16], and extracting template
from WebPages [17]. Our work, on the other hand, is closely related to the literature that examines how to improve
website navigability through the use of user navigation data. Various works have made an effort to address this question
and they can be generally classified into two categories [11]: to facilitate a particular user by dynamically reconstituting
pages based on his profile and traversal paths, often referred as personalization, and to modify the site structure to ease
the navigation for all users, often referred as transformation. The literature considering transformations approaches
mainly focuses on developing methods to completely reorganize the link structure of a website. Although there are
advocates for website reorganization approaches, their drawbacks are obvious. First, since a complete reorganization
could radically change the location of familiar items, the new website may disorient users [18].Second, the reorganized
website structure is highly unpredictable, and the cost of disorienting users after the changes remains unanalyzed. This is

because a website’s structure is typically designed by experts and bears business or organizational logic, but this logic
may no longer exist in the new structure when the website is completely reorganized. Besides, no prior studies have
assessed the usability of a completely reorganized website, leading to doubts on the applicability of the reorganization
approaches.
II. LITERATURE SURVEY
Min Chen and Young U. Ryu [1] proposed an approach of mathematical programming model to improve the
navigation effect of the website minimizing changes to its current structure. Their model was particularly suitable for
informational websites whose contents are relatively stable over time. It improves the performance of website rather than
reorganizes and therefore suitable for website maintenance on a progressive basis The Mathematical Programming model
was observed to scale up very well, optimally solving large-sized problems in a few seconds in most cases on a desktop
PC Perkowitz and Etzioni [02] describe an approach that automatically synthesizes index pages which contain links to
pages pertaining to particular topics based on the co-occurrence frequency of pages in user traversals, to facilitate user
navigation. However this method is web personalization
The methods proposed by Mobasher et al. [13], [14], [15] and Yan et al. [16] create clusters of users profiles from
weblogs and then dynamically generate links for users who are classified into different categories based on their access
patterns. These methods are web personalization based.
Nakagawa and Mobasher [13] develop a hybrid personalization system that can dynamically switch between
recommendation models based on degree of connectivity and the user’s position in the site. For reviews on web
personalization approaches, see [18] and [19].
Web transformation, on the other hand, involves changing the structure of a website to facilitate the navigation for a
large set of users [28] instead of personalizing pages for individual users. Fu et al. [29] describe an approach to
reorganize WebPages so as to provide users with their desired information in fewer clicks. However, this approach
considers only local structures in a website rather than the site as a whole, so the new structure may not be necessarily
optimal.
Gupta et al. [19] propose a heuristic method based on simulated annealing to relink WebPages to improve navigability.
This method makes use of the aggregate user preference data and can be used to improve the link structure in websites
for both wired and wireless devices. However, this approach does not yield optimal solutions and takes relatively a long
time (10 to 15 hours) to run even for a small website.
Lin [20] develops integer programming models to reorganize a website based on the cohesion between pages to reduce
information overload and search depth for users. In addition, a two-stage heuristic involving two integer-programming
models is developed to reduce the computation time. However, this heuristic still requires very long computation times to
solve for the optimal solution, especially when the website contains many links. Besides, the models were tested on
randomly generated websites only, so its applicability on real websites remains questionable.
Lin and Tseng [20] propose an ant colony system to reorganize website structures. Although their approach is shown
to provide solutions in a relatively short computation time, the sizes of the synthetic websites and real website tested in
[20] are still relatively small, posing questions on its scalability to large-sized websites.
There are several remarkable differences between web transformation and personalization approaches. First,
transformation approaches create or modify the structure of a website used for all users, while personalization approaches
dynamically reconstitute pages for individual users. Hence, there is no predefined/built-in web structure for
personalization approaches.
In order to understand the preference of individual users, personalization approaches need to collect information
associated with these users (known as user profiles). This computationally intensive and time-consuming process is not
required for transformation approaches.
Transformation approaches make use of aggregate usage data from weblog files and do not require tracking the past
usage for each user while dynamic pages are typically generated based on the users’ traversal path. Thus, personalization
approaches are more suitable for dynamic websites whose contents are more volatile and transformation approaches are
more appropriate for websites that have a built-in structure and store relatively static and stable contents.
Jia-Ching Ying, Chu-Yu Chin, Vincent S. Tseng [23] proposes a special data structure named Ideal-Tree (Inverted-
data-base Expectable Tree) to avoid the effort of scanning database. Meanwhile, an efficient mining algorithm named
Ideal-Tree-Miner is proposed for mining web navigation patterns with dynamic thresholds. Based on the discovered
patterns, they give a navigation prediction model. Mining Web Navigation Patterns with Dynamic Thresholds for
Navigation
Dean and Henzinger alsPredictiono proposed another simple algorithm to find relevant pages from page similarities.
The page source of this algorithm, however, only consists of the sibling pages of the given page and many important
semantically relevant pages might be neglected. And the similarity between a page and the given page is measured by the
number of their common parent pages, named cocitation degree. The pages that have higher cocitation degrees with the
given page are identified as relevant pages. Although this algorithm is simple and efficient, the deeper relationships

among the pages cannot be revealed. For example, if two or more pages have the same cocitation degree with the given
page, this algorithm could not identify which page is more related to the given page.
Reis et al .used a restricted tree-edit distance to cluster documents and, in it is assumed that labelled training data are
given for clustering. However, the tree edit distance is expensive and it is not easy to select good training pages.
Crescenzi et al.focused on document clustering without template extraction.
They targeted a site consisting of multiple templates. From a seed page, WebPages are crawled by following internal
links and the pages are compared by only their link information. However, if web pages are collected without considering
their method, pages from various sites are mixed in the collection and their algorithm should repeatedly be executed for
each site. Since pages crawled from a site can be different by the objectivity of each crawler, their algorithm may require
additional crawling on the fly.
[25] Propose a hierarchical network search engine that clusters hypertext documents to structure given information
space for supporting various services like browsing and querying. All hypertext documents in a certain information space
(e.g one website) were clustered into a hierarchical form based on contents as well as link structure of each hypertext
document. By considering about links within the same website, related documents in the same website could be grouped
into one cluster. However, our target is not general situation but search results classification, which clusters search results
into more narrow and detailed groups.
There are several remarkable differences between web transformation and personalization approaches. First,
transformation approaches create or modify the structure of a website used for all users, while personalization approaches
dynamically reconstitute pages for individual users. Hence, there is no predefined/built-in web structure for
personalization approaches.
In order to understand the preference of individual users, personalization approaches need to collect information
associated with these users (known as user profiles). This computationally intensive and time-consuming process is not
required for transformation approaches.
Transformation approaches make use of aggregate usage data from weblog files and do not require tracking the past
usage for each user while dynamic pages are typically generated based on the users’ traversal path. Thus, personalization
approaches are more suitable for dynamic websites whose contents are more volatile and transformation approaches are
more appropriate for websites that have a built-in structure and store relatively static and stable contents.
Dean and Henzinger [30] also proposed another simple algorithm to find relevant pages from page similarities. The
page source of this algorithm, however, only consists of the sibling pages of the given page and many important
semantically relevant pages might be neglected. And the similarity between a page and the given page is measured by the
number of their common parent pages, named cocitation degree. The pages that have higher cocitation degrees with the
given page are identified as relevant pages. Although this algorithm is simple and efficient, the deeper relationships
among the pages cannot be revealed. For example, if two or more pages have the same cocitation degree with the given
page, this algorithm could not identify which page is more related to the given page. Crescenzi et al. studied initially the
data extraction problem and Yossef and Rajagopalan introduced the template detection problem. Previously, only tags
were considered to find templates but Arasu and Garcia-Molina observed that any word can be a part of the template or
contents. they detects elements of a template by the frequencies of words. Reis et al. used a restricted tree-edit distance to
cluster documents and, in it is assumed that labeled training data are given for clustering. However, the tree edit distance
is expensive and it is not easy to select good training pages. Crescenzi et al. focused on document clustering without
template extraction. They targeted a site consisting of multiple templates. From a seed page, WebPages are crawled by
following internal links and the pages are compared by only their link information. However, if web pages are collected
without considering their method, pages from various sites are mixed in the collection and their algorithm should
repeatedly be executed for each site. Since pages crawled from a site can be different by the objectivity of each crawler,
their algorithm may require additional crawling on the fly. Lerman et al. Proposed systems to identify data records in a
document and extract data items from them. Zhai and Liu proposed an algorithm to extract a template using not only
structural information, but also visual layout information. Chakrabarti et al. solved this problem by using an isotonic
smoothing score assigned by a classifier. For XML documents, Garofalakis et al. solved the problem of DTD extraction
from multiple XML documents. While HTML documents are semi structured, XML documents are well structured, and
all the tags are always a part of a template. The solutions for XML documents fully utilize these properties. In the
problem of the template extraction from heterogeneous document, how to partition given documents into homogeneous
subsets is important.
III. IMPLICATION OF THIS SURVEY
This survey contributes to the literature on improving web user navigation by examining issue from a new and
important angle. As time passes and the need for information changes, websites also need to be regularly maintained and
improved. However, the current literature focuses on how to restructure a website. Webmasters need to carefully balance

the tradeoff between desired improvements in the user navigation and the changes needed to accomplish the task when
selecting appropriate path thresholds. This is particularly important when a website is improved on a regular basis.
IV.CONCLUSIONS
In this paper we have presented a survey of research papers analysing different techniques for Web mining, the
application of data mining and Web transformation techniques. A change in any of the constituent technique for Web
Mining accounts for a change in paradigm consequently affecting the approach of web analyser to examine the data for
identifying the navigation pattern. So a good understanding of the data preparation technique
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