ANIS AZUMA BINTI CHE ZULKIFLI BTAL15040462 BACHELOR OF SCIENCE COMPUTING IN SOFTWARE DEVELOPMENT UNIVERSITI SULTAN ZAINAL ABIDIN

1. ONLINE SHOP
RECOMMENDATION
SYSTEM
ANIS AZUMA BINTI CHE ZULKIFLI
BTAL15040462
BACHELOR OF COMPUTER SCIENCE IN
SOFTWARE DEVELOPMENT
UNIVERSITI SULTAN ZAINAL ABIDIN
SUPERVISOR – DR ZAHRAHTUL AMANI BINTI
ZAKARIA

2. INTRODUCTION
People prefer online shopping over traditional shopping experience
where they need to stop by at the shop because it is convenient and
price are more comparable
One of the biggest challenges of the company is to organizes, stores
and retrieves relevant and important information about their branch’s
details
Customers also can get all the information needed by just clicking
in the website easily see the details about their desire shop then
proceed with the buying process
Customers also can search the store by states, so they can easily
buy the product by cash delivery for free or little bit charges.
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3. OBJECTIVE
To design a user-friendly website that helps in recommending the
best store for user
To validate and verify a good choice from the system to the user
To develop a website that apply a decision tree algorithm in
recommending the store based on user’s interest
To evaluate and analyse the recommendation provided by the de
cision tree algorithm
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4. SCOPES
Scopes of the system and users
Online Shop
Recommendation
System
Register, login, add, up
date and delete module
Scope of the system
Simply display recently
visited items to facilitate
the user
This authority can only
be accessed by the
authorized module
ADMIN - Can manage, update and
control all the functions in the system
Scope of the user
CUSTOMER - Can register and login
into the system and then manage
their profile. Can search and view the
shops. Can also read the reviews
and feedback from other customers
CLIENT - Can register and login into
the system and manage their profile.
They can add criteria of their shop
and update the products details
Can filter out from a
recommendation system
an item that the user has
already searched

5. SYSTEM LIMITATION
Does not support online payment. Customer needs to pay m
anually and update or inform the seller about the payment
Does not provide booking function for customer to buy the product
since they need to directly deal with the seller
This system only support web-based and can only be accessed through
web browser
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6. DESIGN AND
FRAMEWORK

7. SYSTEM FRAMEWORK
As for client, they first need to register into the
system and need to be approved by admin.
Client can manage their profile, details and
information, criteria, product and status of
their product.
Customer first must be needed to register
before proceeding into the login page and
accessing the system.
After succeed logging in, customer can simply
fill in the desired criteria in the section
provided.
After that, the result of recommendation will
be displayed in the next page and customer
can simply view and choose the best shop
based on their preferences.

8. CONTEXT DIAGRAM (CD)
There are three main entities that
connected to the system which
are Admin, Client and Customer
Customer has three data flows
(two outgoing, one ingoing)
Admin has three data flows
(one outgoing, two ingoing)
Client has six data flow
(four outgoing, two ingoing)

9. DATA FLOW DIAGRAM (DFD)

10. ENTITY RELATIONSHIP DIAGRAM (ERD)
There are five entities involved in this ERD which are admin,
client, customer, product and criteria.
Client and criteria related to each other by one-to-one
relationship.
Client and product are related to each other by one-to-many
relationship.
While for customer and criteria they are related by one-to-many
relationship.

11. PROOF OF
CONCEPT

12. Homepage for both client and customer can access

13. About Us

14. Homepage for client

15. Client can add and update their shop criteria into the system

16. Client can view the report

17. Customer can choose the criteria that they prefer in the form provided

18. Customer can view the result of the recommended shop based on the criteria that they
chose

19. SOLUTION
COMPLEXITY

23.  We start a Decision Tree with a decision that we need to make
 Draw a small square to represent this towards the left of a
large piece of paper
 From this box draw out lines towards the right for each
possible solution, and write that solution along the line
 At the end of each line, consider the results
 If the result of taking that decision is uncertain, draw a small
circle
 If the result is another decision that we need to make, draw
another square
 Squares represent decisions, and circles represent uncertain
outcomes
 If we have completed the solution at the end of the line, just
leave it blank
 Starting from the new decision squares on our diagram, draw
out lines representing the options that we could select
 From the circles draw lines representing possible outcomes

24.  Now we are ready to evaluate the decision tree.
 This is where we can work out which option has the
greatest worth to us.
 Start by assigning a cash value or score to each
possible outcome.
 Estimate how much we think it would be worth to
us if that outcome came about.
 Next look at each circle (representing an uncertaint
y point) and estimate the probability of each
outcome.
 If we use percentages, the total must come to 100
% at each circle.
 If we use fractions, these must add up to 1.
 If we have data on past events we may be able to
make rigorous estimates of the probabilities.
 Otherwise write down our best guess.

25.  Once we have worked out the value of the outcomes
and have assessed the probability of the outcomes
of uncertainty, it is time to start calculating the values
that will help us make our decision.
 Start on the right hand side of the decision tree, and
work back towards the left.
 As we complete a set of calculations on a node
(decision square or uncertainty circle), all we need to
do is to record the result.
 We can ignore all the calculations that lead to that
result from then on.
 Where we are calculating the value of uncertain
outcomes (circles on the diagram), do this by
multiplying the value of the outcomes by their
probability. The total for that node of the tree is the
total of these values.

26.  When we are evaluating a decision node, write down
the cost of each option along each decision line.
 Then subtract the cost from the outcome value that we
have already calculated.
 This will give us a value that represents the benefit of
that decision.
 Note that amounts already spent do not count for this
analysis – these are "sunk costs" and (despite emotion
al counter-arguments) should not be factored into the
decision.
 When we have calculated these decision benefits,
choose the option that has the largest benefit, and
take that as the decision made.
 This is the value of that decision node.

27. SUMMARY
The design of the system have been described in detail with techniques
such as and data flow diagram, entity relationship diagram, database
design, framework and user interface.
The related processes and data stores also have been stated and
elaborated.
Analysis of user needs and requirements have been studied and
the design of the proposed system can be described.
The success of the system depends on the primary processes
described in this chapter.
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28. REFERENCES
P. Li and S. Yamada. A Movie Recommender System Based on Indu
ctive Learning. In IEEE Conference on Cybernetics and Intelligent Sy
stems, 2004.
G. Adomavicius and A. Tuzhilin. "Toward the Next Generation of Rec
ommender Systems: A Survey of the State-of-the-Art and Possible E
xtensions," IEEE Transactions on Knowledge and Data Engineering,
vol. 17, Jun. 2005, pp. 734-749.
N. Manouselis and C. Costopoulou, “Analysis and Classification o
f Multi-criteria Recommender Systems,” World Wide Web: Interne
t and Web Information Systems, vol 10, Apr. 2007, pp.415–441.
Shikha M. & Dinesh C.J, 2012. A Comparative Analysis of Differe
nt Types of Models in Software Development Life Cycle. Internati
onal Journal of Advanced Research in Computer Science and Sof
tware Engineering, 2(5). Pages 285-290.
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29. Thank you