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Movie datastructure2
- 1. Optimizing Profit for the Movie Theatre 김가영 박수현 박현도 성훈 이재완 Keynote by : 진겸
- 2. > 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
- 3. 1 Setting Goals 2 Fixing(reinitializing) variables 3 Analyzing and predicting the box office 4 Scheduling the movies
- 4. Goals
- 5. Profit to variety Discard immeasurable states
- 7. Variables 변수
- 10. Movie’s Inherent Specification Quantitive Value 주연배우 인지도 감독 인지도 배급사 조연배우 인지도 Social Buzz 관객 평점 프랜차이즈 개봉전 평점 Need to modify variables for actual analysis! * variables will be continuously modified and changed 개봉후 1주차 관객수 (매출점유율)
- 11. 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
- 12. Variables need correct scope, standard, interval
- 13. Where to get?
- 14. 관객 점유율 http://www.kofic.or.kr/kofic/business/infm/ introBoxOffice.do 평점 http://movie.naver.com/ 감독 인지도 http://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 관련 http://www.kobis.or.kr/kobis/business/mast/mvie/ searchMovieList.do 버즈량 http://snsbuzz.com/m_index.php https://www.tibuzz.co.kr/ 영화관 입장권 통신전산망
- 15. How can we get meaningful result?
- 17. Linear regression Logistic regression Bass defusion Multiple regression
- 18. 2차원 Data 선형적, 양의 상관관계가 있음
- 20. Linear Regression 종속변수 (Output) 예측값 Parameter Vector(What we want to find) 독립변수(Data input)
- 21. Variable 1 Variable p Coefficient 1 Coefficient 2 Multiple Linear Regression 변수가 좀 많을때…
- 22. Mean Squared Error Optimization with differentiation on w Prediction Real Value
- 24. OLS regression
- 25. Non linear data linearalized by raising powers of variables What if data is not linear?
- 26. Movie 4 : 1.56 Movie 1 : 1.17 Movie 2 : 1.03 … Movie 3 : 0.91 Regression Results >
- 27. Movie 4 : 1.56 Movie 1 : 1.17 Movie 2 : 1.03 … Movie 3 : 0.91 Need to be sorted by order Regression Results >
- 28. Movie 4 : 1.56 Movie 1 : 1.17 Movie 2 : 1.03 … Regression Results > Movie 3 : 0.91 Need to be sorted by order Tournament Tree Choosing the winner Total sort time of ( )logO n n
- 29. Movie 1 Movie 2 Movie 3 …… Movie n 31% 24% 20% 15% Movie 4 90% Cutting point Movie 1 : 1.56 Movie 2 : 1.17 Movie 3 : 1.03 … Regression Results >
- 30. 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 Tulk Configure parameters on our own Not enough training data
- 32. Time Scheduling
- 33. Movie 1 Movie 2 Movie 3 31% 24% 20% 15% Movie 4 Screen1 Screen2 Screen3 Screen4 Screen5 Screen6
- 34. Hypothesis > People are general and like to follow trend (People in Gangnam) > No special cases > All days are same.
- 35. Screen1 Screen2 Screen3 Screen4 Screen5 Screen6 peak time afternoon morning late night forenoon Division by Timezone Different 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
- 36. Screen1 Screen2 Screen3 Screen4 Screen5 Screen6 peak time Movie1 Movie1 Movie1 Movie1 Movie1 Division by Timezone Too complex Need to differ the ratio of each movie per time zone Movie2 Movie2 Movie2 Movie3Movie3 Movie2 Movie1 Movie4 Movie1 Movie1 Movie1
- 37. 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
- 38. 600 400 300 300 200 150 Division by Screens (with variety of seats) Movie1 Movie1 Movie1 Movie2 Movie2 Movie3 Movie4 Movie3 Movie2 Problem with this is that, “time” is not considered at all.
- 39. Combining time zone with screen division On 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 Movie4 Movie3 Movie2 Movie1 Movie2
- 40. 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)
- 43. Reference sites
- 44. Thank you