Enhancing Cluster Quality by Using User Browsing Time
Rehab Duwairi Khaleifah Al.jada'
Dept. of Computer Information Systems Dept. of Computer Science
Jordan Univ. of Sc. and Technology Jordan Univ. of Sc. and Tech.
Irbid, Jordan Irbid, Jordan.
The World Wide Web currently contains billions of documents; this causes difficulty in
finding the desired information by users. Many search engines come out to help users finding their
desired information but search engines still return hundreds of irrelevant web pages that do not fulfill
the user's query. Several search engines use clustering to group documents that are relevant to the user's
query before returning them to the user, but there is no document clustering algorithm that has an
accuracy that can prevent retrieving irrelevant documents. In this research, the researchers have
introduced a new technique to enhance cluster quality by using user browsing time as an implicit
measure of user feedback, rather than using explicit user feedback as in previous research and
techniques. The major contributions of this work are: investigating user browsing time as an implicit
measure of user feedback and proving its efficiency, enhancing cluster quality by using a new
clustering technique that is based on user browsing time, and developing a system that tests the validity
of the proposed technique.
Keywords: Web Mining, Data Mining, Implicit Feedback, Clustering, Filtering, Search Engine.
The Word Wide Web is the largest known collection of hypertext data, it is dynamic
in nature and grows exponentially with time; for example, the number of documents
published on the web reached 550 billion documents in 2001 . This vast quantity of data
makes finding proper information a tedious and time consuming activity for web users.
Most web users are using search engines to find their desired information on the web,
however, search engines usually retrieve large number of web pages as a result of a user
query; most of these pages are irrelevant to the user. Typically users browse through the first
two pages of the returned list of pages. Search engines employ ranking algorithms that aim to
placing the most relevant pages to the user’s query into the top of the list of returned pages.
Many recommender systems (i.e. systems that aim to enhance search results to satisfy
user's queries) which are based on user feedback were developed to solve the aforementioned
problem, but these techniques use explicit user feedback which is costly in terms of time and
resources. An implicit user feedback can be driven by using different features like click-
through, time spent on the page (browsing time), exit type, added to favorites, and scrolling
count . Clustering can be employed by search engines to group together similar pages;
subsequently these clusters can be utilized in ranking pages or in improving the presentation
styles of search engines. In this research we investigate user browsing time as an implicit user
feedback measure to enhance web search accuracy by enhancing the quality of web pages'
clusters. The objective is to enhance web search precision based on user browsing time. The
experiment shows an incentive enhancement on search precision after applying the proposed
2. Background and Related Work
Explicit user feedback does not apply easily in different real-world information and
filtering applications . Recently many researchers are discussing and proposing techniques
to infer user feedback implicitly. A study done by Steve Fox and others  shows that
implicit user feedback can substitute for explicit user feedback. This study proposes different
measures that can be used to infer user feedback.This research uses two of these measures
namely: duration in seconds, and visits (number of visitors) to calculate average browsing
time to implicitly infer user feedback.
Another technique is proposed in  where documents are represented in different ways
(such as top ranked sentences (TRS), document title, summary sentence, sentence in context,
and full text document). The idea of this technique is that the user starts by browsing TRS of
the document if s/he is interested in it, s/he will browse the next representation which is the
page title, and if s/he is still interested, s/he goes to the next representation until s/he reaches
the document. The relevance degree is determined by observing the browsing path. As the
user goes deeply in this path, the more relevant this document is.
Semi-supervised clustering allows users to provide their feedback . The technique uses
initially an unsupervised clustering algorithm to cluster documents, and then the user browses
the resulting clusters and provides feedback to the system by saying:
•“This document doesn’t belong in here”.
•“Move this document to that cluster”.
•“These two documents shouldn’t be (or should be) in the same cluster”.
To illustrate semi-supervised clustering, assume that two documents x1and x2were clustered
into the same cluster by the employed algorithm, when the user browses these documents the
user says “these two documents shouldn’t be in the same cluster”, consequently the clustering
algorithm must modify the distance measure in the next iteration to increase the distance
between x1and x2to separate them. Figure 1 illustrates how semi-supervised clustering works.
Figure 1 (a): Semi-supervised
clustering before user feedback
Figure 1 (b): Semi-supervised
clustering after user feedback
User feedback-driven clustering technique as described in  uses three phases to cluster a set
Phase 1:Pre-clustering (generation of fine-grained clusters): the system partitions a given
document collection into small clusters based on the distance between documents. The
complete-linkage hierarchical agglomerative clustering is used as a basic clustering algorithm
to generate fine-grained clusters.
Phase 2:Supervision phase (user feedback): in this phase, two types of document bundles
(i.e. group of documents) are created: a positive bundle and a negative bundle. These bundles
are developed by a relevance feedback program from interviews with the user. The interview
program starts by extracting a set of documents randomly from a set of pre-clusters, and then
the user determines relevant documents to put them in the positive bundle and irrelevant
documents to put them in the negative bundle.
Phase 3:Re-clustering (assigns each of the pre-clustered document to its nearest positive
document bundle): in this phase each of the pre-clustered documents is assigned to the
positive bundle in which its nearest document is found. During assigning of pre-clusters to the
positive bundle; the local centroid of the cluster is updated. At the same time the negative
bundle is observed to prevent assigning any documents in the negative bundle to the positive
bundle. If such document is assigned to the positive bundle then it is re-assigned to another
cluster that has its second nearest document.
3. AUser-Enhanced Clustering Technique
This section demonstrates in details the proposed work. It uses three phases, namely:
Creating Initial Clusters, Filtering, and Re-Clustering.
Creating Initial Clusters:
To demonstrate the effectiveness of user browsing time as a means of improving
cluster quality, a clustering algorithm to create initial clusters is needed. The researchers
choose Frequent Item-set based Hierarchical Clustering (FIHC) algorithm to create the initial
clusters . The FIHC was selected based on the following reasons:
1. This document clustering algorithm produced consistently high quality clusters.
2. As shown in  it gives the best results when it is compared with other document
3. It could be applied to a large and complicated data set (like web pages).
4. Its output is an XML file, which can be easily converted into other data formats.
FIHC was modified to satisfy this research requirement; in particular the modification
was in its output to obtain document vectors and global frequent items which are needed in
the re-clustering phase of the proposed technique.
The main idea of the proposed technique is to derive user feedback from user
browsing time. The assumption is that when a user spends a long time in viewing a web page,
it means that this web page is relevant to the user's query. In contrast, if a user spends a short
time in viewing a web page, it means that this web page is irrelevant to that user.
To get user browsing time for each web page which is returned as a result to user
query, software called "Genius Filter" was prepared, it is used as a search engine and as
observer to the user browsing time. Genius Filter has an interface where the user can type his
query and as a result, a set of web pages is returned.
The user writes his query, which must be a cluster label, and starts the searching
process, the software searches in the clusters about a cluster label that matches the user's
query and returns a list of links to web pages found in this cluster. When the user clicks on
any link, the target web page opens in a browser window. The timer starts when the web page
is completely loaded. To gain a high accurate user browsing time, the timer stops when a web
page is minimized or is inactive because it isn't currently being browsed by the user.
Several users were trained on using the Genius Filter and were allowed to submit
queries to this filter. For every query and for every returned page (that was opened by the
user) the browsing time was recorded in a database. If several users open the same page, then
the browsing time for that page is the sum of all browsing times.
After a period of browsing web pages (at least one week) the average browsing time
is calculated and any web page that has average browsing time less than a specified threshold
(determined experimentally) is considered irrelevant, otherwise, it is considered relevant to
the user's query.
Figure 2 part (a) shows the first phase of the proposed methodology; C1,C2,and C3
are the initial clusters and solid circles represent documents, Figure 2 part (b) shows the status
of the initial clusters after the filtering phase; solid squares represent irrelevant web pages
based on their average user browsing time which didn’t achieve the threshold. All irrelevant
web pages were moved to a special cluster called irrelevant-cluster.
Figure 2 (a): First Phase of the proposed
Figure 2 (b): Status of the clusters after
the filtering phase
The purpose of the third phase of this technique is to determine the best cluster for each irrelevant
web page. The rank of relevant web pages inside clusters based on user browsing time was investigated.
The top Nrelevant web pages in each cluster are used to represent their clusters in the re-clustering phase.
The top Nrelevant web pages from each cluster were taken and combined in one group as one
data set, and then the KNearest Neighbor (K-NN) algorithm  was applied to determine the nearest K
web pages in the data set to each irrelevant web page. The cluster which contains most of the Knearest
web pages is considered as the best cluster of the irrelevant web page.
4. Experimentation and Result analysis
4.1 Dataset and Experiments
To evaluate the proposed technique, the researchers have pre-specified that the dataset will
consist of 5 classes which are TOEFL,Islam,Java,Computer, and Sport.To create the data set, a
collection of URLs relevant to these five topics is created by using Visual Web Spider . After filling
these URLs into an MS Access database, Genius Downloader reads each URL from the database and
saved its target web page on the hard disk; the last step is concerned with converting each web page into a
document (i.e. removing images and multimedia controls) this was done by using Total HTML Converter
. The number of collected pages per class is shown in Table 1.
Table 1: Distribution of pages to classes
Experiments have been done using 30 Pentium IV client PCs and one PC was used as a database
server. The operating system used was Windows XP. Also .NET Framework was installed on client PCs
to enable them to run the (Genius Filter). On the server side, MS SQL Server 2000 Enterprise Edition was
The experiment starts by running Frequent Itemset-based Hierarchal Clustering (FIHC) program
on the data set to get the initial clusters. Table 2 shows the distribution of web pages to clusters. Cluster
labels were determined manually to match original class labels, these labels were determined by counting
the documents that belong to the same original class, and then the cluster label is set to the class name that
most of the cluster’s documents belong to it. For example, given a cluster "C"; its label will be TOEFL if
it contains many documents that talk about TOEFL. After creating the initial clusters by using the FIHC
algorithm, the result was converted from an XML file to a Relational Database; also document vectors,
frequent global items and cluster frequent items were stored in the database.
Table 2: Distribution of documents to clusters
after applying the FIHC algorithm.
Class Label Number of
Cluster Label Number of
After creating the initial clusters and populating the database, the filtering phase starts. In this
phase, (Genius Filter) was run for a duration of two weeks on 30 PCs.During this period, the number of
visits achieved was 3642 for all web pages in all clusters. The developed software stored user browsing
time for each web page. After the two weeks have passed, the average browsing time was calculated for
each web page as follows:
The software requires a pre-specified threshold. This value is used to determine relevant from
irrelevant pages. The main issue in choosing this threshold is that it must raise precision in clusters to
emphasize that each irrelevant web page is discovered. Five different values as thresholds have been used:
10, 20, 30, 40, and 50 seconds but the experiments show that 40 seconds is the best threshold because it
gives the highest precision.
At the end of this phase, a high precision is expected in all clusters because all irrelevant web
pages were discovered and removed. In contrast, low recall is expected in all clusters because irrelevant
web pages are not re-assigned to their proper clusters. So, to determine if this phase is a success the focus
was on precision before and after this phase.
The re-clustering phase comes after the filtering phase. In this phase both precision and recall
were analyzed; so we asses clusters quality based on the F-measure. The re-clustering phase is done
firstly as described in Section 3, and then an optimization that investigates the URL address of the web
page to determine its best cluster was utilized. This approach is simple and it says that if an URL address
of the web page contains any cluster label then this web page is re-clustered to that cluster, if there are
more than one cluster label in the URL address of the web page, then the web page is re-clustered by
using the KNN algorithm as described in section 3.
4.2 Result Analysis
Figure 3 shows the quality of the initial clusters. Low quality is demonstrated in all initial
clusters. This result reflects the weakness of document clustering techniques which are used to cluster
web pages with regard to the fact that the researchers have used FIHC which is one of the best document
clustering algorithms as described in .
TOEFL Islam Java Computer Sport
Cl ust er s
F -M e a s u re
Figure 3: F-measure values of the initial clusters
Figure 4 shows the clusters precision after the filtering phase. It shows a high precision of
all clusters because most of the irrelevant web pages were discovered and removed.
0.29 0.34 0.3 2 0.28
TOEFL Islam Java Com puter S port
P r e c i s i o n
Precision before filtering
Preci si on after fi lte ring
Figure 4: Precision of the clusters before and after filtering
Figures 5, 6, and 7 show the enhancement of clusters quality after the re-clustering phase with
different values of Kand N(used in KNN algorithm) without optimization. The best enhancement
occurred when K=5 and N=8 as shown in Figure 8 which compares the average of F-measure values in
all clusters with different Kand Nvalues.
TOEFL Islam Java Computer Sport
Clu ster s
F - m e a s u r e
Fmeasure of initial clus ters
Fmeasure after reclustering
Figure 5: Enhancements of clusters’ quality
after re-clustering with K=3 and N=5
TOEFL Islam Java Computer Sport
F - m ea s u re
Fmeasu re of init ial c lus ters
Fmeasu re after rec lus tering
Figure 6: Enhancements of clusters’ quality
after re-clustering with K=5 and N=8
To view the efficiency of the proposed optimization technique, which employs the URL of a webpage
to deduce its cluster label, the best quality of the clusters after re-clustering phase without optimization
(at K=3 and N=5) was compared with re-clustering with optimization as shown in Figure 9. The
comparison shows that the optimization technique enhances the quality in all clusters.
0.24 0.18 0. 12
TOEFL Islam Java Computer S port
F- m eas ur e
Fmeasure of initi al clus ters
Fmeasure after recl usteri ng
Figure 7: Enhancements of clusters’ quality after
re-clustering with K=9 and N=10
Average F-measure of all clusters
0.606 0.6 60 0.650
initial After filtering K=3,N=5 K=5,N=8 K=9,N=10
Phases (Reclustering wi thout optimizati on)
F m e a s u r e
Figure 8: A comparison between average F-measure
values with different Kand Nvalues.
Figure 10 shows the comparison between the quality of the initial clusters and the quality of the
clusters after re-clustering with optimization at the best K and N values which reflect the enhancement
degree after using the technique proposed in this paper.
F-measure values with K=5,N=8
TOEFL Islam Java Com puter S port
Clu ster s
F - m e a s u r e
Fmeasu re without opti miz ati on
Fmeasu re with opt imi zat ion
Figure 9: A comparison of F-measure values at
K=5 and N=8 with and without optimization
F-measure before and after reclustering with optimization
0.24 0. 18 0.12
TOEFL Isl am Java Comput er Sport
F - m e a s u r e
Fmeasure of Initial c lusters
Fmeasure after reclustering with
K= 5,N= 8
Figure 10: Results of clusters’ quality enhancement
5. Conclusions and Future Work
This research contributes a new technique to enhance clusters’ quality and web search
precision; it depends on deriving user feedback from user browsing time and also using user browsing
time to determine the most relevant web pages which will be used to re-cluster irrelevant web pages.
The current technique has several new features namely:
1. User feedback is gathered implicitly (i.e. without the knowledge of the user).
2. It is generic; as it can be used as part of any clustering algorithm to enhance the quality of
3. It can be added to offline clustering algorithms or online clustering algorithms.
In the current version of the developed software, pages that remain in the irrelevant-pages
cluster after the re-clustering phase are not further processed (i.e. the software does not attempt to infer
their cluster label). This is because the current version deals with a closed set of cluster labels;
therefore, any page that does not belong to any of the predefined cluster labels is left in the irrelevant-
pages cluster. The researchers plan to extend the software to treat such cases, and consider other types
of implicit user feedback.
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