A Restaurant Recommendation System using Pearson

Correlation for Similarity Measurement

Present by : Arif Akbarul Huda, S.Si, M.Eng

background

2011 2012 2013 20140

5000

10000

15000

20000

25000

30000

35000

The growth of restaurant in Indonesia

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

INFORMATION OVERLOAD

For some people, the overload of information become difficult specially how to manage it and finding which we need.

(A. Zanda, 2012)

SOLUTION

Recommender Systems

RELATED WORK

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

In fact, the importance of taste allow person to select a food in accord

with desires (Arthur Guyton)

The Fact of Taste

✔ Taste => chemical reaction

✔ 5 basic of taste => Sweet, Sour, Salty,

Umami, Bitter

Arthur Guython (Textbook of Medical Physiology, page 665)

Proposed Algorithm

attribute

BitterSalty

SavorySour

SweetSauceSpicyMeat

vegetable

BitterSalty

SavorySour

SweetSauceSpicyMeat

vegetable

User preferenceFoods taste character

attributeitem

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

attribute

foodsUser

preferenceMie 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?

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)

Pearson Correlation

Similarity measurement

atributitem

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

atributitem

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

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

bitter

sweetsavory

salty

sour

spicy

sauce

meat

vegetable

Soto Ayam Kampung

pre

fere

nsi

pe

ng

gu

na

r(soto ayam, preferensi pengguna)=0.789

√0.658∗√1.242=0.8727

Finding a similarity...

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

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….

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

Thank you