Conference on Fundamental and Applied Science for Advaced Technology as Presenter (ICIAMATH 2016) @ UAD - Jogjakarta

1. A Restaurant Recommendation System using
Pearson
Correlation for Similarity Measurement
Present by : Arif Akbarul Huda, S.Si, M.Eng

2. background

4. 2011 2012 2013 2014
0
5000
10000
15000
20000
25000
30000
35000
The growth of restaurant in Indonesia

5. http://www.statista.com/statistics/254456/number-of-internet-users-in-indonesia/

6. INFORMATION OVERLOAD
For some people, the overload of information become difficult specially how to
manage it and finding which we need.
(A. Zanda, 2012)

7. SOLUTION

8. Recommender Systems

9. RELATED WORK

10. Nugroho [1]⁠ telah berhasil membuat
sistem rekomendasi restoran dengan
mempertimbangkan kebutuhan kalori
penggunanya. 20014
Arief [8]⁠ membangun sebuah
sistem rekomendasi wisata di
wilayah Yogyakarta dengan
pendekatan collaborative-filtering
dan penyaringan informasi
berdasarkan lokasi. 2012
Chu [5]⁠ memperkenalkan sistem rekomendasi
restoran yang dapat mengenali perilaku diet,
pola makan dan kesukaan makanan
bersayur pada penggunanya. 2013
Daraghmi [14]⁠ memberikan kontrobusi dalam
pembuatan sistem rekomendasi restoran dengan
mempertimbangkan agama, budaya, alergi,
riwayat kesehatan, dan aktifitas diet
penggunanya 2013
Liu [13]⁠ memerkenalkan teknik rekomendasi restoran
dengan cara memperhatikan opini dan rating yang
diberikan penggunanya. 2013
2011 2012 2013 2014 2015

11. In fact, the importance of taste allow
person to select a food in accord
with desires (Arthur Guyton)

12. The Fact of Taste
✔ Taste => chemical reaction
✔ 5 basic of taste => Sweet, Sour, Salty,
Umami, Bitter
Arthur Guython (Textbook of Medical Physiology, page 665)

13. Proposed Algorithm

14. attribute
Bitter
Salty
Savory
Sour
Sweet
Sauce
Spicy
Meat
vegetable
Bitter
Salty
Savory
Sour
Sweet
Sauce
Spicy
Meat
vegetable
User preference
Foods taste character

15. attribute
item
Soto Ayam
Kampung
User
preference
bitter 0.00 0.00
sweet 0.70 0.63
savory 0.60 0.60
salty 0.20 0.23
sour 0.00 0.07
spicy 0.00 0.43
sauce 1.00 0.67
meat 0.80 0.83
vegetable 0.70 0.57

16. attribute
foods
User
preference
Mie Ayam
Super
Jumbo
Soto Ayam
Kampung
Soto
Campur
Rica-rica
Mentok
Tengkleng
Kambing
bitter 0.00 0.00 0.00 0.00 0.00 0.00
sweet 0.50 0.70 0.70 0.40 0.40 0.63
savory 0.40 0.60 0.50 0.40 0.40 0.60
salty 0.20 0.20 0.20 0.10 0.10 0.23
sour 0.00 0.00 0.20 0.00 0.10 0.07
spicy 0.50 0.00 0.40 0.70 0.60 0.43
sauce 0.80 1.00 1.00 0.70 0.60 0.67
meat 0.80 0.80 0.60 1.00 1.00 0.83
vegetable 0.30 0.70 0.70 0.00 0.00 0.57
Which food should be recommend?

17. Similarity measurement
● Eudiclane distance
● Cosine similarity
● Pearson Corellation
“Pearson correlation tends to give better result in situation
where data not well normalized.” (T. Segaran,2007)

18. Pearson Correlation

19. Similarity measurement
atribut
item
Soto Ayam
Kampung
preferensi
pengguna
bitter 0.00 0.00
sweet 0.70 0.63
savory 0.60 0.60
salty 0.20 0.23
sour 0.00 0.07
spicy 0.00 0.43
sauce 1.00 0.67
meat 0.80 0.83
vegetable 0.70 0.57

20. atribut
item
x y
bitter 0.00 0.00
sweet 0.70 0.63
savory 0.60 0.60
salty 0.20 0.23
sour 0.00 0.07
spicy 0.00 0.43
sauce 1.00 0.67
meat 0.80 0.83
vegetable 0.70 0.57
Similarity measurement

21. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
bitter
sweet
savory
salty
sour
spicy
sauce
meat
vegetable
Soto Ayam Kampung
preferensi
pengguna
r(soto ayam, preferensi pengguna)=
0.789
√0.658∗√1.242
=0.8727
Finding a similarity...

22. Range value note message
0.80-1.00 Very high correlation recommended
0.60-0.79 korelasi tinggi Maybe you liked
0.40-0.59 Low correlation Try it
0.20-0.39 Very low correlation -
0.00-0.19 No correlation -
<< -1.00 berkebalikan -
The range of correlation

23. Adityo Nugroho
● 2 year chef at Hotel Royal Ambarukmo
● 4 year chef at Hotel 101 Yogyakarta
● 2 year as menu consultant at The Real
Steak House Batam
● for 3 years till now has own catering
our expert….

24. Conclusion
● Pearson Correlation formula can be used for
finding a recommended foods appropriate to
user preference
● A food need to be extracted into nine attributes
to identify its characteristic

25. Thank you