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Understanding User Search Behavior of Humkinar Urdu Search Engine

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Search engines have become inevitable in the current digital information age. Different search engines such as Google, Bing, Yahoo, etc., provide access to the most relevant information present on the World Wide Web to users. These search engines not only require the infrastructure to crawl the World-Wide-Web regularly but also need a framework to gather user metadata to understand user search behavior for improving user experience. In addition, user metadata is required to perform business analytics and digital forensics. User information like IP address, location, type of device, response time, user website activity, etc., help us to know about user navigational pattern. In this paper, we present a user search behavior study of regional search engine called Humkinar Urdu Search Engine (USE) by integrating an open source web analytics application “Matomo”. We collect metadata of Humkinar users for about 35 months. Summary reports generated by the tool show different analyses which can help to effectively monitor the search engine. Furthermore, we present subjective test results and feedback to highlight the preferences of USE users. The analysis and survey can be used to improve the overall performance of Humkinar Urdu search engine in terms of ranking and personalization.
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Understanding User Search Behavior of Humkinar Urdu Search Engine
Nazish Azam, Hafiz Muhammad Shafiq, Muhammad Amir Mehmood
Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan
University of Engineering and Technology, Lahore, Pakistan
{nazish.azam, hafiz.shafiq, amir.mehmood}@kics.edu.pk
Abstract
Search engines have become inevitable in the current
digital information age. Different search engines such
as Google, Bing, Yahoo, etc., provide access to the most
relevant information present on the World Wide Web to
users. These search engines not only require the
infrastructure to crawl the World-Wide-Web regularly
but also need a framework to gather user metadata to
understand user search behavior for improving user
experience. In addition, user metadata is required to
perform business analytics and digital forensics. User
information like IP address, location, type of device,
response time, user website activity, etc., help us to
know about user navigational pattern. In this paper, we
present a user search behavior study of regional search
engine called Humkinar Urdu Search Engine (USE) by
integrating an open source web analytics application
“Matomo”. We collect metadata of Humkinar users for
about 35 months. Summary reports generated by the
tool show different analyses which can help to
effectively monitor the search engine. Furthermore, we
present subjective test results and feedback to highlight
the preferences of USE users. The analysis and survey
can be used to improve the overall performance of
Humkinar Urdu search engine in terms of ranking and
personalization.
1. Introduction
In recent years, search engines have turned into a
significant source of multi-domain data. Our knowledge
source has moved from books and papers to web,
predominantly because of the way that search engines
give a wide variety of relevant data in a couple of
seconds [1]. About 98.8% Internet users utilize search
engines to get required information [2]. There are many
search engines available in different languages for
public, e.g., Google [3], Yahoo [4], Bing [5], Baidu [6],
DuckDuckGo [7], and many others [8]. “Baidu” is
specifically designed for Chinese region, “Yandex” is a
well-known search engine in Russia and similarly, there
are many other search engines available in different
languages.
38% of all Americans use a search engine, 31% read
news online, and 30% peruse the Internet just for
entertainment. During this online activity, users leave
“digital footprints” with their internet service provider
(ISP) or search engine, disclosing their interests [9].
Collection of user information is necessary as
government agencies and parties in civil litigation
regularly ask technology and communication
companies to turn over user data. In Pakistan, out of
around 205 million population, about 76% have mobile
phone subscriptions, 37 million people are active social
media users, and an estimated 22% of the population
uses Internet [10]. Other than this, user information
helps to improve user experience of website visitors.
Urdu Search Engine (USE) [11], named as
“Humkinar”, is a practical step to encourage research in
Urdu and facilitates such community who prefers to
search and get information in Urdu. On the basis of
above discussion, we have used a monitoring tool to
make USE better with respect to design, development,
content, and ranking. USE team needs to know what
their visitors are doing on site, where do they click, what
content they read and which links they follow. To attract
more people on USE, it is required to make it perform
efficiently by giving as much minimum delay as
possible.
For website performance improvement, user
behavior analysis is an important factor. It shows the
interests of the user, and its engagement can be
increased by upgrading most visited sections. For this
purpose, a large variety of solutions are available as
products or services, e.g., Matomo, AWStats, Elogic,
Google Analytics, and many more [12]. In most cases,
one has to append a small snippet of JavaScript in web
pages where user monitoring is required [13]. Also, user
activity analysis on a website helps to check the security
of a website indirectly. Another important fact to keep
in mind is that no one can find out about what your
clients need except the clients themselves. So why not
ask them? Our aim is to improve the user experience of
incoming visitors, that is why we are analyzing user
activities and their interests regarding USE.
In this paper, we describe the design, integration, and
usage of our user tracking framework. Our main
objective includes collecting user tracking details for
performance betterment, ranking, and personalization of
USE. We use an open source web analytics tool known
as “Matomo” (formerly Piwik) [14]. For user survey, we
made a questionnaire and got the feedback from 87
users. Our key findings in this study, for last 35 months
are mentioned below:
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· 23,022 people visit USE and total viewed pages are
117,439.
· Total searched queries are 54,710 out of which
15,694 are single word queries and the most
searched query is “Pakistan”.
· About 84.06% of visitors belong to Pakistan and
24.4% used GNU/Linux OS.
· Average page load time of USE is 1.6403s, average
network latency is 0.5116s, and average server
serve time is only 0.0064 seconds.
· From user survey, we found that 70.1% of users
know how to type in Urdu.
· From design point of view, 23% of users gave us 8
points showing a positive impression.
· 59.8% of users said that the design and features of
USE are easy to use for searching and reading Urdu
content.
The remainder of the paper is organized as follows:
Section 2 describes the related work. In Section 3, some
tools are discussed which are applied to USE. Section 4
presents design and implementation. In Section 5, we
discuss the results obtained from the tool. Next, we
present a user survey of USE showing the feedback of
users in Section 6. Finally, Section 7 summarizes the
whole discussion.
2. Related Work
Famous search engine Google has developed a web
analytics application named as Google Analytics. In
article [15], a case study has been done using Google
Analytics showing prominent features, literature
review, real life application of the software and
guidelines for the first time users of Google Analytics.
Another article states that all search engines track user
behavior and recent development shows that search
engines try to integrate results from different collections
into their results to guide their users for relevant results
[16]. This is how users can be guided to quality content
based on personalization functionality. In another paper
[17], the authors have proposed a new ranking algorithm
for user-oriented web page ranking. They did it by
tracking the user’s time spent on web page and compare
it with Google’s PageRank algorithm. The study made
in [18], shows that the authors used AWStats and
Google Trends to visualize the statistics comprising of
number of unique visitors, page views, keywords, origin
of search, and geographic trends.
Eye-tracking analysis of user behavior in WWW
search engine has been done which investigates how
user interacts with result pages, browsing pattern and
views [19]. A quantitative study has been made to
explore that how the behavior of the Google users can
help web masters to improve their techniques to be in
top results on Google [20]. Search engines capture
users’ activities in the search log, which is stored at the
search engine server. An interface is proposed and
developed by [21] which acts as a layer between Google
and the searcher. This framework captures users’
queries before redirecting them to Google.
For large volume of user data, an intelligent system
is required to analyze the user behavior and show trend
prediction. Discovery of user information allows web
based organizations to predict user access pattern and
helps in future developments [22]. A methodological
framework was proposed in the study [23], which
predicts purchase behavior of websites audiences.
Instead of targeting individual user interests and
activities, they profile websites audiences.
Web server logs provide information like traversal
from one page to another, storing user IP address and all
the related information. In [24], a study has been done
in which authors have found different statistics such as
most visited web-pages, user IPs with most visits, and
type of errors users have to face, etc., using
WebLogExpert tool. Similarly in [25], authors have used
both web client data as well as web server logs to build
an automated data mining and recommendation system
for web usage via KNN classification method. User
click stream data was obtained via web client and other
information such as IP address, user name, server name,
etc., were obtained from web server logs.
The analysis of user behavior also helps in building
a better recommendation system for users while
searching on website. For this purpose, [26] has
proposed a new method through semantic enhancement
by analyzing web access logs. The
Table 1: Comparison of Google Analytics and
Matomo
Feature Google Analytics Matomo
Vendor Google Matomo
Edition Single Self/Cloud
hosted
Installation No Easy to install
User interface Easy Easy
Link to website Addition of
tracking ID
Addition of
JavaScript
Addition of plugin Not allowed Allowed
Number of users Limited Unlimited
Re-marketing
integration Google Ads None
Data freshness Not guaranteed All time
Data Limited Unlimited
authors have built three models for this purpose, two of
them are for domain knowledge of website and third one
is an ontology based model. They have shown that their
proposed method enhances the web-page
recommendation system and performs better than the
most advanced web mining methods, i.e., PLWAP-
Mine. Furthermore, [27] has examined web-server logs
to find the number of visitors and their behavior to
enhance the usability of an educational website. For this
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analysis, the authors have used logExpertLite tool and
found different statistics such as total hits, users,
bandwidth usage, unique IPs, etc., for 5 days of the
week. In this study, they have discussed how to increase
the accessibility and usability of a website from these
metrics.
3. Tools
There is a large variety of web monitoring tools
available on the Internet like AWStats, eLogic, Google
Analytics, ShinyStats, Webalizer, and many others.
Here, first, we provide a brief description of Google
Analytics and Matomo. Next, we discuss the rationale
behind our choice of analytical platform for studying
user behavior of USE.
3.1. Google Analytics
It is a service based solution which is provided by
Google to track traffic of a website. Free version is
perfect for small companies and provides multiple data
collection options across websites. Enterprise version is
required for integration with Google BigQuery,
Salesforce, advanced analysis, and access to raw data. A
maximum of 200 number of views per property can be
utilized while enterprise solution gives limit to 400
numbers. In order to use it, one just needs a Google
account and has to append a small JavaScript code
provided by Google Analytics in the footer of web
pages. Google Analytics Spreadsheet add-on is
available to access and manipulate data using Google
spreadsheet. Native re-marketing is done with Google
Ads. Google Ads, AdSense, and Search Console are
used for native data on-boarding [28].
3.2. Matomo
Matomo (formerly Piwik) is an open source web
analytics platform which provides detailed insights
about user activities and their engagement on a website.
Real-time data updates can be received containing
detailed view of visitors and their activities. It also
provides row evolution feature which allows to compare
current and past metric data for various reports. Page
transitions can be seen through it which help to view
what visitors did before, and after viewing a specific
page. The dashboard of this platform is customizable
and can be extended by adding a wide variety of widgets
and plugins. Major advantage of this tool is that one has
complete control over it as this can be installed on web
server side. Using Matomo APIs, data accessibility is
easy. Advance reports can be collected by adding
manual queries in the database. Adding custom
dimensions and settings is another feature provided by
Matomo. It gives privacy protection by not sharing user
data with advertising companies. It uses database for
archival and storage. Data formatter is used to format
the data in presentable format [14]. Many other features
of this tool are discussed later in this paper.
3.3. Comparison
Table 1 provides a brief comparison between
Matomo and Google Analytics. Although Google
Analytics is easy to use and there is no need for any type
of installation, but being a search engine website, USE
should own the complete user data, privacy and web
hosting. Also, there are bandwidth and user limitations
while using Google Analytics services. Moreover, it is
not allowed to customize available plugins. Due to such
restrictions, we have to use Matomo that is an open-
source solution and easily customizable
4. Design and Implementation
In this section, first, we briefly discuss USE, its
major components, and features. After that, we provide
brief description about hosting and dashboard
customization of Matomo. Finally, in the end, we
discuss integration of tool with USE along with data
acquisition and rendering.
4.1. Urdu Search Engine
USE is an Urdu language search engine which can be
accessed at www.humkinar.com.pk. USE is comprised
of three major components: Cloud Infrastructure (CI),
Information Retrieval (IR), and Search Management
(SM). CI is responsible for incremental web crawling
services, development, testing and deployment of the
work. On the other hand, IR performs linguistic and
textual analysis on raw content while SM deals with
building of indexes for available documents and apply
ranking algorithms to present meaningful results to the
user. Figure 1 presents a workflow diagram for USE. It
has a distributed crawler that crawls and indexes web
documents continuously. Customized ranking
algorithms are being used to display most relevant and
trending results to the user. An adaptable web interface
is developed to serve results according to the query of
user. For indexing and search solutions, “Apache Solr”
is used by USE. Primary source of information storage
and retrieval is Apache Hadoop framework. USE has
developed their own filters for checking language, age,
size and profanity of the documents. It has its own
developed summary module to present summarized
result according to the query of the user. Another major
achievement of USE is that it has given SMS facility to
users so that they can get latest and updated news by
using SMS facility through their smartphones.
To keep all the above mentioned functionalities safe and
updated, there is a dire need to monitor all the activities
on USE. Unique requirements of USE include self-
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hosted tool so that it can have total control. Based on
these requirements, a monitoring tool is designed for
debugging, user behavior analysis, trends, ranking,
personalization, and security checking. The next section
briefly describes the design and implementation of the
tool developed for USE.
4.2. Self-Hosting of Matomo
In our case, we use “self-hosted” approach to install
Matomo on our web-server. Before its installation, it is
required to make sure that you have a web server, shared
hosting or dedicated server. If web server is not
available then “Cloud Hosted” Matomo can be used for
user analytics. By fulfilling all requirements, we
successfully integrated version 3.7.0 of Matomo with
Figure 2: Matomo structure
USE. It has a user-friendly graphical interface which is
also customizable. We customized different plugins
according to the requirements.
4.3. Dashboard Customization of Matomo
After providing login credentials, dashboard of
Matomo can be accessed and there we have quick links
to various sections of the analytics tools. The real-time
section shows two subsections namely “real-time IP”
and “searches”. This is a custom plugin that shows only
the summary of currently active IP addresses and
searches made. Dashboard is the main analytics section
of Matomo which can be customized according to the
requirements. Different metrics can be used to track user
behavior like evolution over the period, reports, device
type, operating system, top searches, best performing
pages, visitor logs, out-links etc. Default analytics
features of Matomo are somehow limited in their usage.
For example, default location provider of Matomo
identifies the location of a user based on the language
they use which is not very accurate. To tackle this
problem, we added GeoIP2
Table 2: Yearly based analytics of Humkinar
(October 24, 2016 - October 01, 2019
Attributes 2016 2017 2018 2019
Total visits 859 4,560 9,661 7,942
Unique visits 244 2,104 3,640 5,390
Total page
views 6,948 22,267 71,896 16,328
Total search
keywords 3,916 11,101 33,926 5,767
Bounce rate 23% 46% 42% 63%
Total outlinks 227 1,379 7,642 5,747
Table 3: General statistics
Attributes Values
Total visits 23,022
Unique visits 11,378
Average page load Time 1.6403s
Average time spent by visitor 14 min 21s
Total page views 117,439
Total searches 54,710
Total outlinks 14,995
(PHP version) which uses GeoIP2 database and
MaxMind’s PHP API to find accurate location of the
user. Another custom analytics feature was added in
Matomo which helps us to record the document
position. This position is then used for ranking of search
results in Humkinar. Similarly, instead of using default
Figure 1: Architectural diagram of Humkinar USE
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reports, we have used custom reporting APIs, not
limited in usage, to get our desired information in JSON
or other formats.
4.4. Integration and Data Acquisition
After installation of Matomo on USE platform, a
script is provided by Matomo that we append at the
footer of those web pages that should be monitored. It
logs all activities being carried out on the frontend and
sends to back-end monitoring server. For USE, it
includes information such as entered queries, click
events, number of new and recurring users, IP, browsers
information etc. Figure 2 shows a high level view of
work-flow diagram for user monitoring at USE. Client
enters a query on search engine and information about
user and his query is stored in Matomo stats collector.
This data is then sent to database for archival and
storage. Data formatter converts the received data into
presentable format and passes it to web dashboard. User
is not disturbed at all in the whole process and he sees
only search results on frontend of USE as a reply.
Furthermore, in this study, we have analyzed data of
October 24, 2016 to October 01, 2019.
5. Results
In this section, we present our findings for user
behavior monitoring on USE with Matomo. First section
describes yearly based statistics of Humkinar. Then we
discuss other metrics like visitor browser, device type,
event logs etc. After that, we discuss about the metrics
that are very important for search engine websites such
as searched keywords, clicks, user Geo-location, and
website performance for different sections etc. Table 3
shows general statistics of USE.
5.1. Yearly Based Analytics
Table 2 shows statistics for year 2016 (start from 24
October), 2017, 2018, and 2019 (up till 01 October). For
each year, we are presenting attributes and their
respective values. Attributes include total visits, unique
visits, total page views, total search keywords, and total
out-links. The statistics show that total number of visits
is increasing every year, i.e., in 2016 total visits were
counted 859 and in 2019 total visits count is 7,942. It
can be seen that bounce rate is increasing every year as
the users are increasing. The reason is that as USE is not
only a search engine but a portal as well and provides
latest content on its home page. Hence, it is quite
obvious that some users just visit USE to read the latest
content and leave the page after reading. Overall, these
statistics show that USE is getting more attention year
by year.
5.2. Visitor Browser
Information about the visitor browser is really
supportive for solving the browser inconsistencies.
Designers need to keep in mind that cross browser
testing is necessary to avoid the most common problems
[29]. Hence on the basis of this point, we obtained the
information about it to avoid any cross-browser
inconsistency. We found that 55.89% of visits are from
Chrome browser, so USE developers should pay more
attention to this for display of USE. Other browsers
include Firefox, Opera, Safari, and others. More than 15
different types of browsers and their types are found in
our record while tracking the users of USE, e.g., Mobile
Safari, Chrome Mobile etc.
5.3. Device Type
We observe that more than 80% of the users use
desktop/laptop to visit USE. Other devices include
smartphone, tablet and phablet. This information is
really helpful as it suggests to improve the site visibility
with respect to desktop devices. Device type
information helps to make the website responsive with
respect to different screen sizes. It is also possible to
show more on large screens and less on small screens.
5.4. Event Logs
These type of logs provide two levels of information,
user queries and corresponding clicks on search results.
It can be used to know user interest on the
Table 4: Number of unique searches for different
tabs
Tab Name Number of unique searches
Web 3,892
Books 872
Islam 1,148
News 1,035
Poetry 966
Sports 270
Videos 559
Wikipedia 266
Famous websites 187
website e.g., most clicked results and corresponding
queries, images, tabs visit etc. Keeping this information
in mind, further changes can be made in these sections
of website to attract more users. In event logs section, a
sample shows that 0.1% of visits contain search term
“Pakistan” and clicks on Urdu Wikipedia outlink.
5.5. Site Search Keywords
Matomo also provides searched keywords
information for each user. We observe that a total of
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54,710 queries are searched and "Pakistan" keyword is
at top. We also analyzed the length of searched
keywords i.e., how many are single word, two words
and so on. Most users search single word query on USE
and their total count is 15,694. Similarly, for two-word,
three-word, and four-word queries, we have frequency
values of 2,036, 1,069 and 548 respectively.
5.6. Website Tabs Usage & Search Statistics
As USE has many sections (tabs) e.g., web, news,
poetry, books, etc., here we present the usage
distribution of each section. Obtained statistics show
that most people visit the home page of USE with about
24% share. Other top visited sections are web, poetry,
Islam, news and videos tabs with a share of 13.5%, 11%,
3.2%, 1.9% and 1.1% respectively. These statistics also
indicate the interest of users on USE at section level. It
also suggests which section should be further improved
to increase user engagement. Similarly, we also collect
information about number of search queries for different
tabs. Table 4 shows unique search statistics in different
tabs of USE. We have mentioned the number of unique
search keywords for each tab. Out of total searches,
9,195 searches are unique keywords.
5.7. Visitor Log
To analyze the user behavior, we made a visitor log
displaying its profile and details as each and every minor
information is important to be logged. Table 5 shows the
user-level details of a sample visitor. It has
Table 5: Visitor profile attributes
Attributes Values
IP address 66.249.93.88
Visitor profile ID 1362e2e13b0b8819
Browser type Chrome mobile
OS type Android 6.0
Device type Smartphone, Motorola
Location United States
Total time spent 3min 34s
Number of actions 5
page views 1
different attributes about the visitor like IP address, user
ID, browser type, Geo-location etc. A sample taken
from record shows that a user from the United States
with IP address 66.249.93.88 visits USE through
Android 6.0 using chrome mobile browser in Motorola
Smart-phone. He spends 3min 34s on USE and performs
5 different actions. He finds 1 item of his choice and
redirects to the respective link. His acti ons include
www.humkinar.com.pk/Poetry, www.punjnud.com and
some other outlinks.
5.8. Website Performance Statistics
The performance monitoring of our website with
respect to page load time, network latency, and server
serve time is also calculated. As it is not affordable to
overlook the significance of website load speed because
clients who are baffled by a slow page speed are
probably going to leave the site. This is why it is
important to improve the website load time to enable
clients to get where they’re speeding up. We found that
average page load time of USE is 1.6403s, average
network latency is 0.5116s, and average server serve
time is only 0.0064s.
5.9. Others
We find that 40 different versions of operating
systems like Windows, Linux, Ubuntu, Android, iOS,
etc., are used to visit the USE. By analyzing these
statistics, we observe that Linux is the most used
Operating System (OS) with 24.4% of the total users.
We also observe that USE visitors belong to more than
50 different countries with Pakistan at the top position
with 84.06% share. Other countries include United
States, Australia, India, Saudi Arabia etc. These
properties may seem less important but they actually
guide the developers to avoid any limitations in their
website. Another important information about the user
is to find the channel type from where he/she is
accessing the site. In our case, we found three channels,
i.e., search engine, websites, and social network. It
means that users are visiting USE through other search
Figure 3: Search platform preference for Urdu content
Figure 5: Urdu typing methods
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engines, from some website redirection, or from any
social network like Twitter, Facebook etc.
6. User Survey for Humkinar Urdu Search
Engine
In this section, we discuss the user survey results and
feedbacks regarding USE. To observe the user behavior
and interest on Humkinar, we conducted a survey in
which different questions regarding the features and
search results of Humkinar were asked. We got a total
of 87 responses from both males and females subjects.
Out of the 87 users, 69% were males and 31% were
females. Most of them belong to the age of 20-30 as
majority of the subjects were students. We asked them
to fill the questionnaire by visiting Humkinar and
checking the features and functionalities step by step
and answer the questions accordingly. It was necessary
to ask them about Urdu typing experience as Urdu
typing is the key functionality for our search feature.
Most of them answered Yes, i.e., 70.1%, while 29.9%
answered in No, which shows that majority of users
already know how to type Urdu. Figure 3 shows that
65.5% of the users said that they use Google to find
Urdu documents while remaining 34.5% use other
platforms to search Urdu content.
Figure 4: Subjective test results Humkinar design
From the design and features point of view, we
prepared a separate section containing questions related
to design view only. To get the overall feedback about
design from a user, we used 1-10 linear scale range, i.e.,
1 shows very bad and the number goes on to 10 showing
very good. Figure 4 shows the chosen values by users
regarding design of Humkinar. Majority users, i.e., 23%
chose scale value 8. 71.3% users voted that they like the
color scheme and presentation of Humkinar frontend.
Humkinar uses Nafees Nastaleeq Urdu font and 97.7%
users liked its rendering style and readability. For Urdu
typing, Humkinar provides three typing methods: 1)
Automatic Urdu Typing 2) On-screen Urdu Keyboard
3) Roman Urdu Typing. Figure 5 provides division of
users based on the Urdu typing methods. For search
results, an individual section was made to ask search
result questions for different tabs of Humkinar. 59.8%
users said that it is easy to find their required results
using this platform, 26.4% selected the option of ”Very
Easy”, and 13.8% of the users found it difficult to search
Urdu content using Humkinar.
Overall, the feedback was satisfying as majority of
the responses were positive. We also got comments
from each and every user at the end of questionnaire and
many useful suggestions were given by them, e.g., add
more sections like cooking, health, horoscope, currency
rates, biography page for famous personalities etc.
Some of them proposed that we should also add voice
search option to find query results. We can conclude that
the overall survey feedback was good enough to
implement new functionalities in Humkinar for the ease
of users and to make it more adaptable.
7. Conclusion
In this study, we analyzed Urdu Search Engine (USE)
user behavior and obtained different statistics. For this
purpose, we have used open-source solution “Matomo”
and customized it according to our requirements. With
this tool, we have analyzed last 35 months user search
behavior on USE. For this interval, our findings show
that USE is visited 23,022 times and total page views
are 117,439. Total searched queries are 54,710, top
query is “Pakistan” and most search queries are single
word query (15,694). About 84.06% visitors belong to a
single country, i.e., Pakistan and most of them used
Chrome browser (55.89%) with Linux (24.4%) OS.
While loading the USE website, total load time is only
1.6403 seconds. By incorporating click information of
visitor for search query, we updated ranking algorithm
of search results. Further, we presented user survey
results, total 87 participants, regarding USE design,
content, and features. It was found that 65.5% users use
Google to search Urdu content. 71.3% users liked the
interface of USE. Overall feedback is agreeable and it is
helpful for us to improve the quality of USE with respect
to design, features, and content. In future, we plan to use
“Matomo" stack personalization” to implement
personalization feature in Humkinar for enriched user
experience.
8. Acknowledgement
This research work was funded by Higher Education
Commission (HEC) Pakistan and Ministry of Planning
Development and Reforms under National Center in Big
Data and Cloud Computing.
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