movie recommender system

1. MINI PROJECT
MOVIE RECOMMENDER SYSTEM
PRESENTED BY
A.PUTEEN KUMAR(20J21A1201)
AMAN PANDEY(20J21A1202)
G.NISHANTH REDDY(20J21A1208)
JOGINPALLY BR ENGINEERING COLLEGE
DEPARTMENT OF INFORMATION TECHNOLOGY
UNDER THE GUIDANCE :
MR .P.SRINIVAS
ASSOCIATE
PROFESSIOR

2. CONTENTS
1. Abstract
2. Introduction
3. Existing System
4. Proposed Method
5. Module
6. Software Requirement Specification
7. System Design
8. System Life Cycle
9. Result
10.Conclusion

3. ABSTRACT
A recommendation system is a system that suggests to users the
products.
The Movie recommendation system suggests movies a user.
By using the dataset and few machine learning algorithms we can
build a Recommendation system.
It suggests those movies which users liked in the past and would
like to watch movies similar to that.
The machine learning approach adopted is Content-Based filtering.

4. INTRODUCTION
Each and every day millions of users use the internet for various
reasons.
So, there is a drastic increase in data.
When the data is not properly processed, the user when looking for
an item has to keep on searching.
This provides inefficient results.
For this situation, recommendation systems provide relevant
recommendations to the user.

5. EXISTING SYSTEM
Netflix : Netflix: Netflix uses a recommendation system to suggest
movies and TV shows to its users based on their viewing history
and preferences.
Amazon prime video : Amazon Prime Video also has a
recommendation system
that suggests movies and TV shows based on the user’s viewing
history and ratings.

6. EXISTING SYSTEM
IMDb: IMDb offers personalized movie recommendations based on
users’ ratings and watch.

7. PROPOSED METHOD
Recommendation Algorithm
Evaluation
User Interface
Data collection
User Profiling
Data Processing

8. MODULES
1. data and recommendation systems
2. providing data to the recommender system
3. approaches for building a recommender system
4. similarity measures

9. DATA AND RECOMMENDATION SYSTEMS
• a) User Behavior Data: User behavior data is all about the user
spending time on a product/ item. This information can be collected
from purchase history, ratings and clicks.
• b) User Demographic Data: User demographic data is all about their
information like date of birth, location, age etc.
• c) Product Attribute Data: Product attribute data contains information
about products themselves like cast, crew, genre etc.

10. PROVIDING DATA TO THE RECOMMENDER
SYSTEM
• Explicit rating: These are provided by the user when they provide
ratings, reviews, likes, feedback.
• Implicit ratings: Implicit ratings are collected when the user interacts
with products. They are inferred from user’s behavior like clicks,
purchases, views
• Product similarity: It is also called item-to-item filtering. It suggests
products based on how much the user would like them.
• User similarity: It is also referred to as user-to-user filtering. This
filtering checks the similarity between two users.

11. APPROACHES FOR BUILDING A RECOMMENDER
SYSTEM
• Popularity based Recommendation system: The products/items are
suggested to the user based on popularity. This is not a good way to
build a Recommendation system.
• Content-based Filtering: A content-based filtering suggests user
those items/products which are similar to which they have bought in
the past. This filtering uses the features of the product to make
recommendations.

12. APPROACHES FOR BUILDING A RECOMMENDER
SYSTEM
• Collaborative-based Filtering: Collaborative filtering recommends
users based on the ratings they provided in the past. It collects the
ratings of all the users, and the similarity is measured against the
users.
• Hybrid approach: A hybrid approach is a combination of content-
based and collaborative-based filtering.

13. SIMILARITY MEASURES
Similarity is measured using the distance between the vectors.
Points that are nearest are similar and points which are farthest
doesn’t relate to each other.
1. Minkowski distance: We use Minkowski distance when the
dimension of the data points are numerical values.
2.Manhattan distance: Manhattan distance calculates the distance
between two points
measured along its right angle.
3.Euclidean distance: The square root of the sum of squares of the
difference
between coordinate points which is given by the Pythagorean
theorem is how we

14. SIMILARITY MEASURES
4.Cosine similarity: It measures the cos angle between two vectors.
When cosine
with a degree is 0, then its value is 1 so this means the data
points are similar. If
the cosine degree is 90 the value is 0, so the data points are not
similar.
5. Pearson correlation: It measures the correlation between two
random variables
and it ranges between [-1,1]. If the correlation value is 1 then it is
a positive

15. SYSTEM REQUIREMENT
SPECIFICATION
SOFTWARE REQUIREMENTS:
operating system : windows 11
Programming language: Python, Tkinter.
Database : PostgreSQL.
Machine learning libraries: TensorFlow, PyTorch, and
Scikitlearn.

16. SYSTEM REQUIREMENT
SPECIFICATION
HARDWARE REQUIREMENTS:
Processor : Intel i3
RAM : 4 GB
Hard disk : 256 GB

17. SYSTEM DESIGN
USE CASE DIAGRAM:

18. SYSTEM DESIGN
Class Diagram:

19. SYSTEM DESIGN
Sequence Diagram:

20. SYSTEM DESIGN
Activity Diagram:

21. SYSTEM DESIGN
E-R Diagram:

22. SYSTEM LIFE CYCLE
Step 1: User has to login/ signup, if the user has already registered
then they can
simply login using their credential

23. SYSTEM LIFE CYCLE
Step 2: When a user is new they have to register and then login. All
the details of the new user gets stored in the database table so
while they try to login it checks for validity.
Step 3: After the user logs in, they would see the following page and
request to recommend movies.

24. SYSTEM LIFE CYCLE
Step 4: After user successfully logs into the system, they can enter
the movie title and ask the system to recommend movies.
Step 5: A function reads the movie-list dataset into pandas
dataframe. It has the 24 Column.
Step 6: As we won’t need all these columns, because some of the
data in this dataset is unnecessary, we only need few main features.

25. RESULT

26. RESULT

27. RESULT

28. CONCLUSION
• Recommendation Systems are widely used in today world for
searching for efficient and reliable information.
• he movie recommendation system in this project recommends
movies to the user based on a few features cast, director,
genres and keywords.

29. THANK
YOU