CAR-RECOMMENDATION-SYSTEM-1-1 (2)
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CAR
RECOMMENDATIO
N SYSTEM
Analysis
Duong Anh Quang
Member
Obijiaku Calisstus
Chisom
Nguyen Thi Phuong
Hang
MOTIVATION AND PURPOSE
REPERTORY GRID
Content
ANALYSIS
FUZZY COGNITIVE MAP
Motivation and
purpose
• The number of vehicles in global market
is increased
• Many cars with similar features get into
market
People will be confused on what to
choose
We built a car recommend system
based on the elements and
constructs.
Repertory grid
Car
Friend
Youtube
company recomme Personal Showroo
1 videos
drive.com nada.com telegraph edmunds. website
ndation
taste
m
7
.au
.co.uk
com
Detailed
Surfaced
Explanation
6
5
1
6
2
1
7
4
1 Explanation
Used by most
Used by few
people
2
4
5
7
5
2
7
4
3 people
Reliable Info
Unreliable
Source
6
3
2
4
4
4
2
1
5 info source
Widely
Not Widely
Accepted
4
2
6
6
6
4
5
2
3 Accepted
Satisfactory
Not
conclusion
7
1
3
6
6
3
6
5
4 Satisfactory
Easy to find
Hard to find
Information
4
2
2
4
4
3
5
7
3 information
Small
Big Database
1
2
3
3
2
3
4
7
5 Database
Not easily
Easy Approach
1
1
1
1
1
1
4
1
6 Approach
Able to
Not able to
recommend
3
5
5
5
5
5
3
7
1 recommend
Feedback
No Feedback
functionality
1
2
3
4
3
2
5
7
5 functionality
Element Analysis (Pearson
Correlation)
1
youtube
1
drive.com
2
nada.com
3
telegraph.co.u
k
4
edmunds.com
5
car company
website
6
friend
7
personal taste
8
showroom
9
2
3
4
5
6
7
8
9
0.26
-0.02
0.38
-0.46
0.31
0
-0.28
-0.30
0.44
0.12
-0.12
0.54
-0.24
-0.04
-0.4
0.30
0.70
0.71
-0.19
0.02
-0.16
0.63
-0.1
0.65
0.16
-0.7
0.55
0.03
0.02
-0.32
-0.7
-0.02
0
0.21
-0.41
-0.32
Element Analysis (Distance
Euclidean)
1
youtube
1
drive.com
2
nada.com
3
telegraph.co.u
k
4
edmunds.com
5
car company
website
6
friend
7
personal taste
8
showroom
9
2
3
4
5
6
7
8
9
8.0
9.27
7.14
6.56
7.42
8.89
11.75
9.95
6.32
7.81
7.55
5.57
9.11
10.0
9.54
6.86
4.36
4.36
8.77
9.59
8.54
4.69
8.25
4.69
8.77
10.1
5.10
7.48
9.22
8.6
10.10
9.54
7.62
8.43
9.06
10.63
Contruct Analysis (Pearson
Correlation)
1
2
3
4
5
6
0.44
7
8
9
10
Detailed
Explaintion
1
-0.05
0.04
0.44
0.40
-0.14
-0.09
0
0.05
Used by most
people
2
-0.48
0.54
0.25
0.20
0.11
0.05
0.15
0.40
Reliable
Source
3
0.1
0.27
-0.36
-0.58
0.15
-0.61
-0.6
Widely
Accepted
4
0.43
-0.17
-0.41
-0.16
-0.03 -0.21
Satisfactory
conclusion
5
0.62
-0.02
0.05
-0.17
0.18
Easy to find
Information
6
0.61
-0.01
0.31
0.69
Big Database
7
0.39
0.16
0.94
Easy
Approach
8
-0.81 0.42
Able to
recommend
9
0.13
Fuzzy Cognitive Map
User
Cause/Effec Numbe Technolog
t
r
y
Service
User Number
0
0
-0.2
Technology
0.3
0
0.7
Service
0.8
0
0
UX
0.6
0
0
UI
0.1
0
0.1
Car data
0.1
0
0
User data
0
0
0.1
Concept
Value
initializati
on
0.3
0.4
0.6
UX
-0.3
0.3
0.9
0
0.15
0.2
0.1
UI
0
0.2
0
0
0
0
0
0.7
0.1
Car data User data
0
0.8
0.4
0.5
0
0.2
0
0.2
0
0
0
0
0
0
0.6
0.3
FCM Diagram
Technology
UX
Service
User Number
UI
User data
Car Data
Formular
• A t is the value of concept at timestep t
• W 0 is the matrix of casual relationship.
• K 2 j is the weight of previous concept value at time t-1
• A
t-1
is the value of concepts at timestep t-1
• K 2 j = 0.5
Sigmoid
Implementatio
n
• The code used for calculate the
concepts value at different
stages
Concepts Value
Step
0
1
1
3
4
5
User
Number
0.3
0.776
0.835
0.845
0.847
0.848
Technolo
gy
0.4
0.55
0.568
0.571
0.571
0.571
Service
0.6
0.636
0.662
0.669
0.67
0.67
UX
0.7
0.747
0.76
0.767
0.769
0.77
UI
0.1
0.532
0.593
0.601
0.602
0.602
Car data User data
0.6
0.3
0.613
0.701
0.629
0.821
0.632
0.838
0.633
0.841
0.633
0.842
=> User number and user data is the most important concept in the car recommendation system
