EFFICIENT AND END TO END TOUR SYSTEM

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 325
EFFICIENT AND END TO END TOUR SYSTEM
Ritik Bobade1, Pratiksha Sawarkar2, Kaustubh Ganorkar3, Piyush Dhore4, Ram Shrirao5,Ms.
Pranita Chaudhary6
1-5
Student, Dept. of Information Technology, SBJITMR, Maharashtra, India
6
Professor, Dept. of Information Technology, SBJITMR, Maharashtra, India
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - The development in information technology has
transformed the tourism industry with modern technology
andchanged it in this new era. Trip planning on the web as
well ason the mobile is the preferred option these days as it
is hassle free and convenient. This end to end tour system
provides suggestions to the users for their next trip using
deep learning‘s Singular Value Decomposition algorithm.
The usergets the top recommended packages to purchase.
Also, the user gets to customize their own package
according to their budgets and interests. Since, the package
includes everythingfrom taking the user from their location
to drop them to theirlocation after tour, it’s called the end to
end tour system.
Key Words: Tourism, Singular value decomposition
algorithm, Deep learning, End to end tour system
1. INTRODUCTION
The progress in technology and the modern business
model has upgraded the tourism industry, in recent years.
The development in informationtechnologyandknowledge
economy, proving the transformation and promotion of
tourism. New and modern technologiesare getting used in
tourism development in the new era. Due to this, science
and technology creates guides for tourism consumption
with tourism needs and formats. In coming future
tourism will be more developed using the information
technology. Also, it will help to change the traditional
mode of tourism consisting of operations and
management to a great extent. The modern technology
will help improve the tourism industry making it more
convenientandhasslefree to the users.
The tourism demand in this era has been increased aswell
as the competition in the tourism destinations as people
now want to explore, thus the market has beenincreased
and with that the need of new modern technologies in
tourism as well. Studies have shown that innovation is the
key to maintain the ability of tourism destinations to
compete in the international market. If the tourism
destinations wants to maintainthemselves in this market
they need innovations in their work to attract tourists.
Even after this much research and studies shown the
need to innovate, stillthere is a lack of development in this
area.
In this web app we are going to build a system that willease
the work of tourists. The user will be getting the
prediction of the destination he should visit based on the
questions answered by him. The user can buy anypackage
of his choice by comparing all the packages provided by
various third party websites. The user can also customize
his own plan according to his interest and budget. The app
will also be having the travel bloggers section where top
bloggers of the countrycanpost their travel plans and guide
their followers accordingly.
2. LITERATURE SURVEY
"A Model for the development of Innovative Tourism
Products: From Service to Transformation”- It assertsthat
attractions are the premise of tourism development as
these are the foremost essential components of tourism
products. Any tourism destination becomes unique and
distinctive based on its cultural as well as environmental
features. Different attractions plays an important role in
making these destinations different from each other.
Attractions arethe most important part of tourism products,
because it motivates the tourists to plan their trips. The
first tourism products that attract tourists to go to
destinations contains physical, environmental, and
sociocultural characteristics. All these features variesfrom
destination to destination and play a major role in the
competition between all the tourism destinations.So, when
there is any new tourism development it should support
all these core features. The tourism products have a
particularity that distinguishes them from other products.
Unlike financial or other services, tourism development
requires a physical stage within the kind of mountains,
beaches, or infrastructure created by choice (e.g., theme
parks). Tourism products are thus linked to specific
locations, so customers need to travel physically to those
places to enjoy these products. The actors involved in the
process aren’t the only influencers in the tourism
development, but also by the features of the destinations
which are distinctive in nature and thus helps in the
development of the tourism products.
“Experience-involvement, memorability and authenticity:
The service provider’s effect on tourist experience”- The
model was supported a managementperspective because
the last word goal is to ‘stage’ tourism experiences per
various authors. Zatori, Smith, and Puczko report that,
through higher-quality interactions, interactive experience

environments and customizable services, service
providers can create favorable external environments
during which deeper, more memorable experiences can
occur. This model focuses on how visitors’ experiences
are triggered by environmental stimuli in a very given
context and maybe positive or negative. The way different
people perceive particular events or situations is strongly
influenced by determining factors inherent to every
person and their personal involvement in experiences.
Thus, different people perceive the identical situation in
totally other ways, and therefore the resulting
experiences are equally diverse.
“Approaches, Issues and Challenges in the
Recommendation Systems: A Systematic Review ”- theyread
66 journals related to the recommendation system
published between 2001 and June 2016 and divided the
recommendation program into the following categories
based on their methods: 1. Collaborative Filtering (CF)
method uses userpreferences having the same options as
the intended user to recommend an item. 2. Content-Based
Filtering (CB) creates an individual user profile that
maintains unique user features that can be used to
recommend items. Features determine the user taste
collected from previous users' preferences. 3. The Social
Filtering System (SF)collects information on users' profiles
and their social / network content. Those are
recommendations made based on targeted users' favorite
friends. It is also called the Community Basedapproach.4.
The People List (DE) filtering system uses demographic
information such as age, country, place etc. To make the
system work it is mandatory to collect demographic
information. 5. The information-based filtering (KB)
method uses a mapping method to give arecommendation.
User preferences are mapped to theproperties of the item
and the system determines whether the item is eligible to
recommend to the user or not. 6. Utility-based approach
(UB) is similar to KBin that depending on the usage of
the item, it is compared to the user requirement and a
recommendation is made. 7. The Hybrid
Recommendation (HR) method uses two filtering
methods in the system. The purpose of this is to coverthe
disadvantages of one method of filtering with the
advantages of another.
More over the authors have mentioned a number of the
analysis ways within the journal paper. This can scalethe
standard of the system; that will further correct the
recommendations area unit or whether or not the
recommendations area unit helpful to the user. The
primary technique mentioned is that the likelihood metric
wherever the reliability of the prediction by the RS is
measured. Graded matrix is another technique wherever
however well is usually recommended things area unit
graded and measured it to live the standardof this rank.
To try to do this the measures that area unit typically used
area unit preciseness, Recall, Mean Average preciseness
etc. If the advice system aims to cut back the amount of
errors then qualitative metric is that the best option. It
measures accuracy, F-measure, alphabetic character
datum, coverage etc. to realize the aim. To live the user
satisfaction level the user satisfaction metrics is employed.
This can be done by assembling assessing the user
feedbacks.
3. PROPOSED WORK
A. Flow of the system:
The user will open the website. After opening thewebsite,
he will take a quiz and answer some questions. The
recommender system will recommend some destinations
based on the answers provided by the user. Now user can
select any location from the recommended ones and select
the package and make payment.
User can also view the top packages provided by the
system. The most viewed or most purchased packageswill
be recommended to the user. The user can choose any
package and make the payment.
The users can also customize their own package. Theyneed
to select all the facilities, consisting of transportation
mode, hotels, resorts, sites to visit, etc.User can customize
their package according to their budget and interests.
After successful creation of package the user need to pay
the estimated amount.
Fig 3.1: Flowchart for end to end tour system

B. Functional Modules:
The whole system is divided into three modules. Theyare
prediction quiz, recommendation system, customization.
1. Prediction Quiz
This involves a quiz, the user needs to answer some
questions and then the system will predict the locations
based on the answers provided by the user.
2. Recommendation system
The recommendation system recommends the top
packages to the user to purchase. It uses the singular
value decompositionalgorithm to recommend topandmost
purchased packages.
3. Customization
In this module, the user getsvarious facilities tochoosefrom
and customize their ownpackage. This is basicallykind of e-
commerce system where user can choose their facilities
and buy them as a package.
Fig 3.2: System Architecture
4. CONCLUSION
This survey helps in developing an approach for efficient
and end to end tour system. The system includestheuseof
machine learning algorithm singular value decomposition
for the recommendation system to recommend the top
packages to the user, hence, providing full satisfaction to
the user. Also it deals with the customization of the
package and reduce the manual work of the user making it
easier for them. Thesystem consists of a blog section, so the
users can learn from others experiences. The system will
help plan the users their tours efficiently and hassle free
accordingtotheir budgets and interests.
REFERENCES
[1] Margarida Custódio Santos, Ana Ferreira, Carlos Costa
and José António C. Santos, “A Model for the Development of
Innovative Tourism Products: From Service to
Transformation”, OECDilibrary, 2020.
[2] Zatori, A. Smith, M.K. Puczko, “Experience-involvement,
memorability and authenticity: The service provider’s effect
on tourist experience”, Science Direct 2018.
[3] Kumar, B. & Sharma, N., 2016, Approaches, Issues and
Challenges in Recommender systems: A Systematic Review,
Indian Journal of Science and Technology, vol 9(47), p.1-12

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