1. MovieGEN: A Movie Recommendation System
Gaurangi, Eyrun, Nan

2. Introduction Related work Implementation Results (Demo) Summary
Outline
Introduction
Related work
Implementation
Results (Demo)
Summary

3. Introduction
Recommendation systems are special types of expert systems
Why necessary?
MovieGEN
Takes in : Personal particulars
Asks questions
Recommends movies

4. Background & Related Work
Other movie recommendation systems – Use movie ratings
We use
Machine learning – Support Vector Machine (SVM)
Cluster analysis – K Means Algorithm

5. Machine Learning based Preference Prediction
Implementation
Machine Learning based Preference Prediction

6. Implementation (contd.)
Data description
Data formatting for SVM regression

7. Implementation (contd.)

8. Implementation (contd.)
Evaluation of recommendation
Movie XML
Question Generator
Question
Feedback to Questions
Recommended Movies
Machine Learning Output Vector
Movie Ranking Algorithm
Ranked Movies
K Means Clustering
Movie Clusters
Chosen Movies
SVM based Machine Learning Model
Movie Ranking
Based on output from SVM
Clustering
K Means
Question generator

9. Implementation (contd.)
Movie Database
300 movies
Information from IMDB
<MOVIE>
<NAME>Titanic</NAME>
<GENRE>Romance</GENRE>
<GENRE>Drama</GENRE>
<STARRING>Leonardo DiCaprio</STARRING>
<STARRING>Kate Winslet</STARRING>
<DIRECTOR>James Cameron</DIRECTOR>
<YEAR>1990s</YEAR>
<AGE_GROUP>Adults</AGE_GROUP>
<GENDER>Both</GENDER>
<RATING>7.2</RATING>
<OSCAR>11</OSCAR>
</MOVIE>

10. Demonstration