Optimizing Profit for the Movie Theatre

김가영 박수현 박현도 성훈 이재완 Keynote by : 진겸

> Which variables are needed(can be used)> Where to find actually useable variables’ database > How to make use of the obtained variables > What kind of Analysis techniques we should use > How to partition the screens in the cinema> What kind of algorithm to use for movie allocation > What kind of data structure to use for storing movies > What kind of data structure to use for scheduling movies

1 Setting Goals 2 Fixing(reinitializing) variables 3 Analyzing and predicting the box office 4 Scheduling the movies

Goals

Profit to variety

Discard immeasurable states

Predicting the Box-office

Variables변수

Variables변수

감독 날씨

등급

공휴일

정치판

지역

좌석수

평점

배우

시간

검색량배급사

마케팅홍보장르

국적

Variables사용가능한 변수

Movie’s Inherent Specification

Quantitive Value

주연배우 인지도

감독 인지도

배급사

조연배우 인지도

Social Buzz관객 평점

프랜차이즈

개봉전 평점

Need to modify variables for actual analysis!

* variables will be continuously modified and changed

개봉후 1주차 관객수 (매출점유율)

배우 인지도 - 근 5년간 출연작품의 관객수(200만)를 바탕으로 discrete한 점수를 매긴다. - 포털/검색엔진/SNS의 검색어 순으로 점수를 매긴다. - 각종 영화관련 수상중 유의미한 수상내역을 바탕으로 점수를 매긴다.

감독 인지도 - 근 5년간 작품의 관객수를 바탕으로 discrete한 점수를 매긴다. - 각종 영화관련 수상중 유의미한 수상내역을 바탕으로 점수를 매긴다.

배급사 - 출시한 작품의 관객수를 바탕으로 discrete한 점수를 매긴다. - More search and discussion needed

프랜차이스 - More search and discussion needed

Social Buzz - Google/Naver Search Results - Twitter API/Exclusive sites

Variables need correct scope, standard, interval

Where to get?

관객 점유율http://www.kofic.or.kr/kofic/business/infm/introBoxOffice.do

평점 http://movie.naver.com/

감독 인지도http://www.kobis.or.kr/kobis/business/mast/peop/searchPeopleList.do

배우 인지도

http://www.kobis.or.kr/kobis/business/mast/peop/searchPeopleList.do http://www.kobis.or.kr/kobis/business/mast/mvie/searchMovieList.do

배급사 관련http://www.kobis.or.kr/kobis/business/mast/mvie/searchMovieList.do

버즈량http://snsbuzz.com/m_index.php

https://www.tibuzz.co.kr/

영화관 입장권 통신전산망

How can we get meaningful result?

Regression Analysis회귀 분석

Linear regression Logistic regression Bass defusion Multiple regression

2차원 Data선형적, 양의 상관관계가 있음

Multiple Linear Regression

Linear Regression

종속변수 (Output) 예측값

Parameter Vector(What we want to find)

독립변수(Data input)

Variable 1 Variable p

Coefficient 1 Coefficient 2

Multiple Linear Regression변수가 좀 많을때…

Mean Squared Error

Optimization with differentiation on w

Prediction Real Value

OLS regression

Non linear data linearalized by raising powers of variables

What if data is not linear?

Movie 4 : 1.56

Movie 1 : 1.17Movie 2 : 1.03

…

Movie 3 : 0.91

Regression Results >

Movie 4 : 1.56

Movie 1 : 1.17Movie 2 : 1.03

…

Movie 3 : 0.91

Need to be sorted by order

Regression Results >

Movie 4 : 1.56

Movie 1 : 1.17Movie 2 : 1.03

…

Regression Results >

Movie 3 : 0.91

Need to be sorted by order

Tournament Tree

Choosing the winnerTotal sort time of ( )logO n n

Movie 1

Movie 2Movie 3

……Movie n

31% 24% 20% 15%

Movie 4

90% Cutting point

Movie 1 : 1.56Movie 2 : 1.17Movie 3 : 1.03

…

Regression Results >

Problems we might interface

Data modification is too hard for analysis

R^2 not at the right precision (lower than 0.65)

그냥 구현을 못함 ㅜㅜ

-> Mechanical TulkConfigure parameters on our own

Not enough training data

Requisite Skillsets

Tensorflow

Scipy

Time Scheduling

Movie 1

Movie 2Movie 3

31% 24% 20% 15%

Movie 4

Screen1 Screen2 Screen3 Screen4 Screen5 Screen6

Hypothesis> People are general and like to follow trend (People in Gangnam)

> No special cases

> All days are same.

Screen1 Screen2 Screen3 Screen4 Screen5 Screen6

peak time

afternoon

morning

late night

forenoon

Division by TimezoneDifferent Weight is put on each time zone and Shelf algorithm is used for fitting in movies into each time zone Also, more popular movies are put more on peak times

Screen1 Screen2 Screen3 Screen4 Screen5 Screen6

peak time

Movie1

Movie1Movie1

Movie1Movie1

Division by TimezoneToo complex Need to differ the ratio of each movie per time zone

Movie2

Movie2

Movie2

Movie3Movie3

Movie2

Movie1 Movie4

Movie1Movie1Movie1

600 400 300 300 200 150

Division by Screens (with variety of seats)Different Weight is put on each screen by actually changing the number of seats After, we sort in the movie with the most weight into the screen with most seats

600 400 300 300 200 150

Division by Screens (with variety of seats)

Movie1 Movie1Movie1

Movie2

Movie2

Movie3

Movie4Movie3

Movie2

Problem with this is that, “time” is not considered at all.

Combining time zone with screen divisionOn top of partition by screens, we can create another layer of time zones. According to time zones, we can switch movies on based of fixed algorithms

600 400 300 300 200 150

Movie1 Movie1 Movie1 Movie2

Movie2

Movie3

Movie4Movie3

Movie2Movie1

Movie2

More discussion is needed on

> How to split the partitions of screens

> What variables should be considered

> Which algorithm should be used on the structure

> What we do with leftover time (how to use it effectively)

Requisite Skillsets

Reference sites

Thank you