Industrial Training Report - II
on
Movie Recommendation System
submitted in partial fulfillment of the requirements
for the award of the degree
of
Bachelor of Technology
in
Computer Science and Engineering
By
Darshika Sharma
Enrollment No. A60205219107
Under the guidance of
Mr. Ashish Tripathi
Trainee Specialist
Department of Computer Science and Engineering
Amity School of Engineering & Technology
Amity University Madhya Pradesh, Gwalior
December 2022
Department of Computer Science and Engineering
Amity School of Engineering and Technology
Amity University Madhya Pradesh, Gwalior
DECLARATION
I, Darshika Sharma , student of Bachelor of Technology in Computer Science and
Engineering hereby declare that the industrial training report entitled “Movie
Recommendation System” which is submitted by me to Department of Computer
Science and Engineering, Amity School of Engineering & Technology, Amity
University Madhya Pradesh, in partial fulfillment of the requirement for the award of
the degree of Bachelor of Technology in Computer Science and Engineering, has not
been previously formed the basis for the award of any degree, diploma or other similar
title or recognition.
Darshika Sharma
Date:
(Enrollment No. – A60205219107)
ii
Department of Computer Science and Engineering
Amity School of Engineering and Technology
Amity University Madhya Pradesh, Gwalior
CERTIFICATE
This is to certify that Darshika Sharma (Enrollment No. A60205219107), student of
B. Tech (C.S.E.) VII semester, Department of Computer Science and Engineering,
ASET, Amity University Madhya Pradesh, has done her practical training entitled
“Movie Recommendation System” under my guidance and supervision during “01
August 2022 – 30 September 2022”.
The work was satisfactory. She has shown complete dedication and devotion to the given
project work.
Date:
(Mr. Ashish Tripathi)
External Examiner
Trainee Specialist
Supervisor
(Prof. Dr. Hemant Kumar Soni)
Head of the Department
iii
iv
ACKNOWLEDGEMENT
I am very much thankful to our honorable Vice Chancellor Lt Gen. V. K. Sharma
AVSM (Retd) for allowing us to carry out my practical (for VII semester) training. I
would also like to thank Prof. (Dr.) M. P. Kaushik, Pro-Vice Chancellor, Amity
University Madhya Pradesh for his support.
I extend my sincere thanks to Maj. Gen. (Dr.) S. C. Jain, VSM** (Retd), HOI, Amity
School of Engineering and Technology, Amity University Madhya Pradesh, Gwalior
for his guidance and support for the selection of appropriate industry for my practical
training. I would also like to thank Dr. Hemant Kumar Soni, Head of Department
(CSE), for his kind concern throughout the practical training.
I am also very grateful to Mr. Ashish Tripathi, Trainee Specialist (CSE),
Department of Computer Science and Engineering, Amity School of Engineering and
Technology, Amity University Madhya Pradesh and Mr. Shekhar Metla, Teachnook
Trainings. My internal and external supervisor respectively, for their constant guidance
and encouragement provided in this endeavor.
I am also thankful to the whole staff of Teachnook Trainings for the co-operation and
giving me friendly environment, which made very comfortable for learning and that
of ASET, AUMP for teaching and helping me always. Last but not the least I would
liketo thank my parents and friends for their constant support.
Darshika Sharma
(Enrollment No. – A60205219107)
v
ABSTRACT
In this hustling world, we all need entertainment to recharge our batteries and recharge
our spirits. Entertainment restores our confidence in our work, allowing us to work more
enthusiastically. We can revitalize ourselves by listening to music or watching movies
of our choice. We can use movie recommendation systems to watch favored movies
online, which are more reliable, because searching for preferred movies will take more
and more time, which one cannot afford to waste. In this paper, a Hybrid approach
combining content-based filtering and collaborative filtering is used to improve the
quality of a movie recommendation system.
The proposed methodology uses Support Vector Machine (SVM) as a classifier and a
genetic algorithm, and comparative results show that the proposed approach improves
the accuracy, quality, and scalability of the movie recommendation system over pure
approaches in three different datasets. The hybrid approach combines the benefits of
both approaches while attempting to eliminate the drawbacks of both.
Keywords: Genres, Entertainment, Movies, Collaborative Filtering, Recommender
System
vi
LIST OF FIGURES
Figure No.
Figure Caption
Page No.
Figure 2.1
Types of Filtering
04
Figure 2.2
Credits Genres, and Keywords Based Recommender
05
Figure 2.3
Different Filtration Strategies
06
Figure 4.1
Content Based Filtering
11
Figure 4.2
Cosine Similarity Graph and Formula
14
Figure 4.3
Code Snippet for front End
16
Figure 5.1
Front end of movie recommendation system
17
Figure 5.2
System Recommend Movies in Search bar
18
Figure 5.3
System Recommend Movies With Same genres,
Keywords, Cast
18
vii
LIST OF ABBREVIATIONS
S. No.
Terms
Expanded Form
1
IBCF
Item Based Collaborative Filtering
2
IDE
Integrated Development Environment
3
IDF
Inverse Document Frequency
4
KNN
K-Nearest Neighbors
5
NumPy
Numerical Python
6
SVD
Singular Value Decomposition
7
SVM
Support Vector Machine
8
TF
Term Frequency
viii
CONTENTS
Content
Page No.
Declaration by student
ii
Certificate by supervisor
iii
Certificate by company
iv
Acknowledgement
v
Abstract
vi
List of Figures
vii
List of Abbreviations
viii
Chapter 1. Introduction
01- 03
Chapter 2. Review of Literature
04-08
Chapter 3. System Requirement Specification
09-10
Chapter 4. Implementation on Project
11-16
Chapter 5. Result and Discussion
17-18
Chapter 6. Testing
19-20
Chapter 7. Conclusion and future Scope
21
Chapter 8. Bibliography
22
ix
Chapter 1
INTRODUCTION
1.1 Relevance of the Project
A recommender system, also known as a recommendation system, is a type of
information filtering system that attempts to forecast a user's "rating" or "preference" for
an item. They are mostly employed in commercial settings.
Movie Recommendation System allows users to input a movie they enjoy and outputs a
list of similar movies. This system is based on the MovieLens dataset, which is a
collection of movie ratings and metadata from the Movie Lens website. This dataset
contains 20 million ratings and 465,000 tag applications across 27,000 movies. These
data were created by 290,000 users between January 09, 1995 and October 16, 2016. A
recommendation system, also known as a recommendation engine, is a model used for
information filtering that attempts to predict a user's preferences and provides
suggestions based on these preferences. These systems have grown in popularity and are
now widely used in fields such as movies, music, books, videos, clothing, restaurants,
food, places, and other utilities. These systems collect information about a user's
preferences and behavior and then use that data to improve future suggestions.
Movies are a natural part of life. There are various types of movies, such as those for
entertainment, those for education, animated films for children, and horror or action
films. Movies are easily distinguished by genres such as comedy, thriller, animation,
action, and so on. Another way to differentiate between movies is by release year,
language, director, and so on. When watching movies online, there are many movies to
choose from in our most popular movies section. Movie Recommendation Systems assist
us in searching for our preferred movies among all of these different types of movies,
thereby saving us time spent searching for our preferred movies. As a result, the movie
recommendation system must be very reliable and provide us with recommendations of
movies that are exactly the same as or most closely match our preferences. Many
businesses use recommendation systems to increase user interaction and improve the
shopping experience of their customers. Customer satisfaction and revenue are two of
the most important advantages of recommendation systems. The movie recommendation
system is an extremely powerful and vital system. However, due to the issues associated
with the pure collaborative approach, movie recommendation systems suffer from poor
recommendation quality and scalability issues.
1.2 Problem Statement
The goal of the project is to recommend a movie to the user.
Providing related content out of relevant and irrelevant collection of items to users of
online service providers.
1.3 Objective of the Project
•
Improving the Recommendation System's Accuracy
•
Enhance the film's quality System of recommendations
•
Scalability is being improved.
•
Improving the user experience
1.4 Scope of the Project
The goal of this project is to provide users with accurate movie recommendations. The
project's goal is to improve the quality of movie recommendation systems, such as
accuracy, quality, and scalability, over pure approaches. This is accomplished through
the use of a hybrid approach that combines content-based filtering and collaborative
filtering. To alleviate data overload, recommendation systems are used as information
filtering tools in social networking sites. As a result, there is a huge opportunity for
research in this field to improve the scalability, accuracy, and quality of movie
recommendation systems. The movie recommendation system is an extremely powerful
and vital system. However, because of the issues associated with the pure collaborative
approach, movie recommendation systems suffer from poor recommendation quality and
scalability issues.
2
1.5 Methodology for Movie Recommendation System
To build a movie recommendation system, the hybrid approach proposed an integrative
method that combined the fuzzy k-means clustering method and a genetic algorithmbased weighted similarity measure. The proposed movie recommendation system
provides finer similarity metrics and quality than the existing movie recommendation
system, but the proposed recommendation system requires more computation time than
the existing recommendation system. The clustered data points can be used as an input
dataset to solve this problem.
The approach proposed here is intended to improve the scalability and quality of the
movie recommendation system. We take a hybrid approach, combining Content-Based
Filtering and Collaborative Filtering so that the approaches can benefit from one another.
We used the cosine similarity measure to compute the similarity between the different
movies in the given dataset efficiently and in the least amount of time, as well as to
reduce the computation time of the movie recommender engine.
Agile Methodology
1. Collecting the data sets: Gathering all of the required data sets from the Kaggle
website. We need movie.csv, ratings.csv, and users.csv for this project.
2. Data Analysis: Ensure that the data sets collected are correct and analyses the data in
the csv files. Checking to see if all of the column Felds are present in the data sets.
3. Algorithms: In our project, only two algorithms are used to build the machine
learning recommendation model: cosine similarity and single valued decomposition.
4. Training and testing the model: once the algorithm implementation is complete. To
get the result, we must train the model. We tested it several times and found that the
model recommends a different set of movies to different users.
5. Project Advancements: At a later stage, we can implement different algorithms and
methods for better recommendation.
3
Chapter 2
REVIEW OF LITERATURE
A movie recommendation system is a system that recommends movies to users based on
their personal preferences. This system can be used to recommend movies to a user based
on their past viewing habits, or to recommend movies to a user based on the viewing
habits of other users with similar preferences. There are many different ways to build a
movie recommendation system. One approach is to use collaborative filtering, which
relies on the ratings of other users to recommend movies to a target user. Another
approach is to use content-based filtering, which relies on the content of the movies
themselves to recommend movies to a target user.
Fig 2.1 Types of Filtering
Content-based filtering is often used in conjunction with collaborative filtering, as it can
help to filter out noisy data. Hybrid systems that combine both content-based filtering
and collaborative filtering are also common. No matter what approach is used, the goal
of a movie recommendation system is to provide the user with a personalized list of
movie recommendations. The movies that are recommended to a user should be based
on their individual preferences, and the system should be able to adapt as the user’s
preferences change over time.
4
There are many different movie recommendation systems available today. Some of the
more popular ones include Netflix, Amazon Prime, and Hulu. Each of these systems uses
a different approach to recommend movies to their users. Netflix, for example, uses a
hybrid system that combines content-based filtering and collaborative filtering. Amazon
Prime uses a content-based approach, while Hulu uses a collaborative filtering approach.
These are just a few examples of movie recommendation systems. There are many others
that are available, and new systems are constantly being developed.
2.1 Credits Genres, and Keywords Based Recommender
It is obvious that the use of better metadata would raise the caliber of our recommender.
That is exactly what we are going to do in this section. The following metadata will be
used to generate a recommender: the director, the three best actors, keywords from the
movie's plot and related genres. We must identify the three most significant features
from the cast, crew, and keyword features. the actors, the director, and the movie's tag
lines. Right now, our data is present in the form of "stringified" lists, we need to convert
it into a safe and usable structure.
Fig 2.2 Credits Genres, and Keywords Based Recommender
The following are benefits of content-based filtering:
•
They can recommend unrated products.
•
By enumerating an item's Content properties, we can quickly demonstrate how
the recommender system functions.
5
•
Content-based recommender systems just require the user in question's rating;
they do not require input from any other system users.
The following are the drawbacks of content-based filtering:
•
It is ineffective for new users who have not yet given any items a rating because
this is because sufficient ratings are needed for the content-based recommender
to accurately assess the user's preferences.
•
No suggestions for unforeseen goods.
•
Limited Content Analysis: If the system is unable to discern between the items
that a user likes and dislikes, the recommendation will not be effective.
Fig 2.3Different Filtration Strategies
2.2 Movie Recommendation System Using Collaborative
Filtering
There are two kinds of collaborative filtering systems:
•
User-based recommender
•
Item-based recommender
6
1. User-Based Collaborative Filtering: User-Based Collaborative Filtering is a
method for predicting the products that a user will like based on the ratings given to
that product by other users whose tastes are similar to the target user's.
Collaborative filtering is a popular technique for creating recommendation systems
on websites. We can define user-based collaborative filtering as a method of making
automatic predictions (filtering) about the interests of a user by collecting preferences
or taste information from many users (collaborating).
User-based collaborative filtering algorithms are commonly based on similarity
between users. For example, the user-based collaborative filtering algorithm could
make a prediction for the target user by identifying the user's nearest neighbors and
aggregating their ratings.
2. Item-Based Collaborative Filtering: Item-based collaborative filtering approaches
build models of users' preferences for different items. This approach can be thought
of as a generalization of the user-based collaborative filtering algorithm.
Item-based collaborative filtering algorithms are commonly based on similarity
between items. For example, the item-based collaborative filtering algorithm could
make a prediction for the target user by identifying the items that are most similar to
the items that the user has already rated and aggregating their ratings.
If you are interested in building a movie recommendation system, there are a few things
you need to keep in mind.
•
First, you need to decide what approach you want to use.
•
Second, you need to gather data on user preferences.
•
Third, you need to develop algorithms that can filter and recommend movies
based on the data you have collected.
•
And fourth, you need to evaluate the performance of your system. These are the
four main steps you need to take if you want to build a movie recommendation
system. Keep these steps in mind, and you will be well on your way to
developing a system that can provide personalized movie recommendations to
your users.
7
Collaborative filtering-based systems have the following advantages:
•
They are content-independent because they depend on user relationships.
•
CF recommender systems can make coincidental recommendations by studying
the behavior of users who share their interests.
•
By taking into account the experiences of others, they can determine an item's
true quality.
Collaborative filtering has the following drawbacks:
•
Early rater problem: Since there are no user ratings on which to base a prediction,
collaborative filtering systems are unable to provide suggestions for new items.
•
Gray sheep: A group with similar features is required for the CF-based system to
function. It will be exceedingly challenging to promote people who do not
consistently agree or disagree to these groups, even if such groups do exist.
•
The sparsity problem: When there are many more items than users, it can be
challenging to locate items that have received adequate ratings.
8
Chapter 3
SYSTEM REQUIREMENT SPECIFICATION
This chapter includes a thorough description of the requirements as well as the hardware
and software needed for the project.
3.1 Hardware Requirements
•
A PC with Windows/Linux OS
•
Processor with 1.7-2.4gHz speed
•
Minimum of 8gb RAM
•
2gb Graphic card
3.2 Software Specifications
•
3.2 Software Specification
•
Text Editor (VS-code/WebStorm)
•
Anaconda distribution package (PyCharm Editor)
•
Python libraries
3.3 Software Requirements
3.3.1 Anaconda Distribution:
With the goal of streamlining the package management system and deployment,
Anaconda is a free and open-source distribution of the Python programming languages
for scientific computing (data science, machine learning applications, large-scale data
processing, predictive analytics, etc.). Conda, a package management system, controls
package versioning. Packages for data science are included in the anaconda distribution
and are compatible with Windows, Linux, and MacOS.
3.3.2 Python Libraries
Some python libraries that are used for analytics are required for computation and
analysis. It requires packages like NumPy, Pandas, Matplotlib, Flask framework, etc.
9
1. NumPy: NumPy is a Python library that adds support for large, multi-dimensional
arrays and matrices, along with a large collection of high-level mathematical
functions to operate on these arrays. The library known as NumPy, or "Numerical
Python," contains multidimensional array objects and a selection of procedures for
handling those arrays.
Arrays can be subjected to logical and mathematical operations using NumPy. It
offers high-performance, simple-to-use data analysis tools. In contrast to the multidimensional array objects provided by the NumPy library, Pandas offers an inmemory 2D table object called a Data frame.
2. Pandas: Pandas is an open-source library designed primarily for working quickly and
logically with relational or labelled data. It offers a range of data structures and
procedures for working with time series and numerical data. The NumPy library serves
as the foundation for this library. Pandas is quick and offers its users exceptional
performance & productivity.
3. Flask: Python is used to create the Flask web application framework. It is created by
Armin Ronacher, the president of Pocco, a global organisation of Python aficionados.
The Werkzeug WSGI toolkit and Jinja2 template engine form the foundation of Flask.
They're both Pocco projects.
4. Streamlit: A Python-based open source app framework is called Streamlit. It enables us
to quickly develop web applications for data science and machine learning. Major Python
libraries like scikit-learn, Keras, PyTorch, SymPy (latex), NumPy, pandas, and
Matplotlib are all compatible with it.
10
Chapter 4
IMPLEMENTATION
4.1 Building a Content Based Recommendation System
This approach recommends related items based on content properties. It does not have a
cold-start problem because it works through content attributes or tags such as actors, genres,
or directors to recommend new movies straight away.
For example, if User X enjoys LOTR and Star Trek movies (action, adventure), our
algorithm will recommend movies with similar content to User X, such as Star Wars.
Fig 4.1 Content Based Filtering
Modeling
Import all the dependencies and load the scraped data.
11
Concatenate title, director, genre, description in to a single feature.
This is because our recommendation system works not only by proposing movies of a similar
genre, but also by combining the cast, director, and plot of the film.
The following step is to vectorize this characteristic in order to calculate similarity.
TF-IDF Vectorizer
The TF (term frequency) is the number of times a given term occurs in a document.
TF(t) = (Number of times term t appears in a document) / (Total number of terms in the
document).
The IDF (inverse document frequency) is the measure of how significant that term is in the
whole corpus.
IDF(t) = log (Total number of documents / Number of documents containing the term t in
it).
The reason we chose TF-IDF is that it gives more weight to less frequent terms and less
weight to frequent terms, which is critical for our model.
12
Cosine Similarity
The next step is to compute the degree of similarity between movies. The movies are
suggested using a simple technique known as Cosine Similarity. Cosine similarity can be
used to calculate the distance between two vectors. It can be calculated mathematically as
the cosine angle between two vectors in a three-dimensional plane. We can also calculate
the Euclidean distance between the two vectors to see how dissimilar or similar they are. In
our situation, one of the vectors is the sought movie, and the remaining movies in the
database are examined as the second vector. As recommendations, the top 5 movies with the
shortest Euclidean distance to the searched movie are displayed.
Cosine Similarity is a method of content-based filtering. It is one of the most widely utilised
approaches in recommendation systems. The characteristics of an object are referred to as
its "content." We can determine whether two items are comparable or not based on these
characteristics. The properties can be database words such as genre, cast names, director
names, description, and so forth. If the attributes match or have a significant degree of
similarity, the two films are considered similar. The idea behind this type of recommendation
system is that if a user enjoyed a specific movie or show, he or she could enjoy a similar
movie or show.
13
Fig 4.2 Cosine Similarity Graph and Formula
We must create logic that accepts a movie title as input and returns the top five related
movies based on cosine similarity.
Singular Value Decomposition
A matrix's Singular Value Decomposition (SVD) is a factorization of the matrix into three
matrices. It possesses some intriguing algebraic characteristics and provides essential
geometrical and theoretical insights into linear transformations. It has several key uses in
data science as well.
Mathematics behind SVD
The SVD of m x n matrix A is given by the formula:
14
A=UWVT
where:
U: m x n matrix of the orthonormal eigenvectors of AAT
VT: transpose of a n x n matrix containing the orthonormal eigenvectors of A^{T}A.
W: A n x n diagonal matrix of the singular values which are the square roots of the
eigenvalues of ATA
To do this, we can write a function that computes the similarity score of our input to other
movies in the corpus and then sorts the scores in descending order to obtain the top 5 movies
with the highest similarity scores. Index 0 in the sim scores variable is removed here so that
the function does not return the same movie that was entered in the input.
Test our recommendation engine
Pickle is a serialization/deserialization package built into Python that allows us to save any
Python object (with a few limitations) to a file. Once we have a file, we may load the model
in a separate process.
15
4.2 Designing a Front End
We can now begin developing a web application now that we have finalised the model.
Streamlit is used in the frontend of our model. Streamlit is a Python open-source package
that makes it simple to create stunning custom web apps for machine learning and data
science.
pip install streamlit
Fig 4.3 Code Snippet for front End
You can test the frontend locally on the command prompt after you've built it.
Make sure you're in the directory that contains all of the files, then enter the following
command into the cmd window.
Streamlit run app.py
16
Chapter 5
RESULTS AND DISCUSSION
Considering that our project is a system for suggesting movies. One may use collaborative
filtering, content-based filtering, or a combination of both to create a movie recommendation
system.
In our project, we used a hybrid approach, which combines both content and form. As well
as collaborative filtering. Both strategies have benefits and drawbacks. Only movies based
on user ratings or likes will be recommended to the user in content-based filtering. The
developed Movies recommendation engine is incredibly user-friendly and simple to use.
When the website loads, the visitor will see a screen where he may enter a movie title to get
more information and recommendations. When the movie title is input, a drop-down list of
suggestions appears to perform auto-complete. When a movie is chosen, the enter button
becomes active. After hitting the enter button, a new page with four sections is loaded. Movie
details, top cast, sentiment analysis on movie reviews, and the top ten suggestions are all
available. Sentiment Analysis is performed using the Multinomial Naive Bayes algorithm
after training and testing the data in a 4:1 ratio. Sentiment Analysis has a 98.77167 percent
accuracy rate.
Fig 5.1 Front end of movie recommendation system
17
Fig 5.2 System Recommend Movies in Search bar
Fig 5.3 System Recommend Movies with Same genres, Keywords, Cast
18
Chapter 6
TESTING
System testing is a collection of tests whose primary goal is to completely exercise the
computer-based system. Although each test serves a different goal, they all endeavor to
ensure that all system pieces have been correctly integrated and are performing their assigned
responsibilities. The testing procedure is carried out to ensure that the product does exactly
what it is designed to do. During the testing stage, the following objectives are attempted:
•
To confirm the project's quality.
•
To locate and eliminate any remaining faults from earlier phases.
•
To prove that the programmed is a viable solution to the original problem.
•
To ensure the system's operational reliability.
6.1 Testing Methodology
There are many different types of testing methods or techniques used as part of the
software testing methodology. Some of the important testing methodologies are:
Unit Testing
The initial stage of testing is unit testing, which is frequently handled by the developers
themselves. It is the process of ensuring that individual code components of a piece of
software are functional and work as intended. In a test-driven environment, developers often
write and execute the tests before passing the software or feature to the testing team. Manual
unit testing is possible but automating the process will shorten delivery cycles and increase
test coverage. Unit testing will also make debugging easier because issues detected earlier
in the testing process take less time to fix than those discovered later in the testing process.
Test Left is a solution that enables advanced testers and developers to use the fastest test
automation tool included in any IDE to shift left.
Integration Testing
After each unit has been extensively tested, it is combined with other units to form modules
or components that are meant to execute specific jobs or activities. These are then tested as
a group through integration testing to ensure that entire components of an application behave
19
as expected, (i.e., the interactions between units are seamless). These tests are frequently
framed by user scenarios, such as logging into an application or opening files.
Integrated tests can be performed by either developers or independent testers and are
typically made up of a combination of automated functional and manual tests.
System Testing
System testing is a black box testing approach used to guarantee that the completed and
integrated system meets stated requirements. Before the product is put into production, the
functionality of the programmed is checked from beginning to end by a distinct testing
team from the development team.
20
Chapter 7
CONCLUSION AND FUTURE SCOPE
7.1 Conclusion
In this project, a hybrid approach is presented by combining content-based filtering and
collaborative filtering, using Singular Value Decomposition (SVD) as a classifier and Cosine
Similarity as the proposed methodology. This approach aims to improve the accuracy,
quality, and scalability of movie recommendation systems. On three separate movie datasets,
existing pure methodologies and the hybrid approach are put into practice, and the outcomes
are compared. Comparative results reveal that the proposed approach is superior than pure
approaches in terms of accuracy, quality, and scalability of the movie recommendation
system. The proposed solution takes less time to compute than the other two pure
approaches.
7.2 Future Scope
In the suggested technique, movie genres have been taken into account; however, in the
future, we should also take user age into account because movie tastes change with age. For
instance, when we are young, we tend to prefer animated films over other types of films. The
memory needs of the suggested solution need to be improved in the future. Here, the
suggested methodology has only been applied to several movie datasets. The performance
can be computed in the future, and it can also be used to the Film Affinity and Netflix
databases.
When a user searches for a movie that he or she has previously seen, the Movies
Recommendation System will suggest the top ten movies that are the most comparable to
the searched movie. Furthermore, the system will display extra movie facts as well as
sentiment analysis on the movie's reviews. All of these capabilities will save the user time
that would otherwise be spent searching for a movie that he or she may or may not enjoy.
Every month, new movies are released, and the movie database continues to grow. This
would allow the system to make more accurate recommendations to the user, increasing
consumer satisfaction.
21
Chapter 8
BIBLIOGRAPHY
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Darshika Sharma
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