MOOD MOVIE - A mood based movie recommendation website
Summary
TLDRMah suana introduces a mood-based movie recommendation system designed to enhance user experience by personalizing content according to current emotions. Built with Python and Flask, it uses machine learning to suggest movies that match the user's mood, offering a more tailored experience than traditional streaming services. The project includes stakeholders like end-users, developers, and machine learning engineers, and focuses on user experience, personalization, and content discovery. The system is scalable and secure, with a user-friendly interface and robust backend technologies.
Takeaways
- 😀 The project is a mood-based movie recommendation system aimed at personalizing movie choices according to the user's emotional state.
- 🎬 It addresses the challenge of choosing what to watch from numerous streaming services by suggesting movies that match the user's current mood.
- 🤖 The system uses machine learning algorithms to analyze user mood inputs and recommend movies accordingly.
- 💻 Built using Python and Flask, the project integrates emotional intelligence with traditional recommendation processes.
- 👨💻 Stakeholders include end users, software developers, machine learning engineers, project managers, and potential collaborators like streaming services.
- 💡 The project's objectives are to enhance user experience, offer personalization, and facilitate content discovery beyond traditional methods.
- 🛠️ Hardware requirements include a capable GPU, a mid-range processor, and sufficient RAM to support the machine learning model training.
- 🌐 The front-end is developed using HTML, CSS, and JavaScript, focusing on user input, mood processing, and movie recommendation functionalities.
- 🔐 Non-functional requirements such as performance, security, scalability, and usability are emphasized to ensure a robust and user-friendly system.
- 📈 The project timeline includes planning, data collection, model development, validation, and deployment phases, with ongoing monitoring and maintenance.
- 📊 Backend technologies include Python for programming, Pandas for data handling, NumPy for mathematical functions, and Scikit-learn for machine learning model accuracy and validation.
Q & A
What is the main objective of the mood-based movie recommendation system?
-The main objective is to enhance the user experience by recommending movies that match the user's current mood, providing a personalized and enjoyable experience compared to traditional methods.
How does the system integrate emotional intelligence into the recommendation process?
-The system uses machine learning to understand the user's mood input and recommend movies accordingly, such as suggesting certain movies for a positive, energetic, or reflective mood.
What are the key features of the mood-based movie recommendation system?
-Key features include personalization based on mood, content discovery to explore new movies, and a user-friendly interface that allows users to input their mood and receive movie suggestions.
Who are the primary stakeholders of the project?
-The primary stakeholders include end users or movie watchers, software developers, machine learning engineers, project managers, and potential collaborators like streaming services.
What are the hardware requirements for the project?
-The hardware requirements include a computer with a capable GPU, a mid-range processor with at least four cores, and sufficient RAM, typically 8 GB.
What front-end technologies were used in the development of the website?
-The front-end technologies used include HTML for layout, CSS for visual presentation, and JavaScript for interactive elements.
Can you describe the function requirements of the website?
-Function requirements include user input for mood, mood processing with filters like mood, language, and genre, and movie recommendation using a trained machine learning model.
What non-functional requirements were considered during the development of the website?
-Non-functional requirements include performance, security, scalability, usability, and maintainability to ensure the system is efficient, safe, can handle growth, is user-friendly, and is well-documented for future maintenance.
What programming language and technologies were used for the backend of the project?
-Python was the main programming language used, with libraries like Pandas for data handling, NumPy for mathematical functions, and Flask for user interaction in the web framework.
How was the project timeline structured?
-The project timeline included planning and preparation, data collection and processing, model development, model validation, and deployment and implementation.
What is the process for a user to get movie recommendations on the website?
-Users can log in, select a mood, and use filters like language and genre to get movie recommendations. They can also search for movies by title.
Outlines
🎥 Introduction to Mood-Based Movie Recommendation System
Mah suana introduces a project that aims to revolutionize movie selection by incorporating emotional intelligence into the recommendation process. The system is designed to understand the user's mood and suggest movies accordingly, using machine learning. Built with Python and Flask, it offers a personalized experience, enhancing user satisfaction and content discovery. The project focuses on improving the user experience, personalization, and content discovery, differing from traditional streaming services that rely on past user history or demographics.
👥 Stakeholders and Project Overview
The stakeholders of the project include end users who benefit from the personalized movie recommendations, software developers responsible for building the system, machine learning engineers who train the models, project managers overseeing the development process, and potential collaborators like streaming services. The hardware requirements are outlined, including a capable GPU, mid-range processor, and sufficient RAM. The project timeline is detailed, starting from planning and preparation, followed by data collection and processing, model development, validation, and finally deployment and implementation.
🛠️ Frontend and Backend Technologies
Julia discusses the frontend technologies used in the project, including HTML, CSS, and JavaScript, which provide the layout, visual representation, and interactivity of the website. The backend is primarily developed using Python, with libraries like Pandas for data handling, NumPy for mathematical functions, and Scikit-learn for feature extraction and accuracy validation. Flask is used for user interaction, combining the frontend with the recommendation model. The project timeline includes phases for planning, data collection and processing, model development, and deployment, with an emphasis on the use of tools like the TMDb API for data collection and processing libraries for data manipulation.
🌐 Demo of the Movie Recommendation Website
Julia Pilu provides a demo of the Mood Movie website, showcasing its features and functionality. The website allows users to log in, select a mood, and receive movie recommendations based on their choice. Filters for language and genre are available, and a search bar enables users to find specific movies. The system uses a machine learning model to recommend movies that match the user's mood, providing movie posters, titles, and descriptions. The demo concludes with a logout option, returning to the homepage for new users to sign up or log in.
Mindmap
Keywords
💡Mood-based Recommendation System
💡Personalization
💡Machine Learning
💡Content Discovery
💡Stakeholders
💡Hardware Requirements
💡Front-end Technologies
💡User Experience (UX)
💡Natural Language Processing (NLP)
💡Flask
Highlights
Introduction to a mood-based movie recommendation system that personalizes movie choices according to the user's emotional state.
The system aims to simplify the overwhelming choice of movies on streaming platforms by suggesting content that matches the viewer's mood.
Utilizes machine learning algorithms to analyze user mood and recommend movies accordingly.
Developed using Python and Flask, showcasing a blend of programming and web development technologies.
The system is designed to understand and respond to the user's current mood, enhancing the movie-watching experience.
Aims to provide a more personalized movie-watching experience compared to traditional methods that rely on past history or demographics.
Facilitates content discovery by suggesting new movies that users may not have considered before.
Identifies key stakeholders including end-users, software developers, machine learning engineers, project managers, and potential streaming service collaborators.
Outlines the hardware requirements necessary for the project, such as a capable GPU, mid-range processor, and sufficient RAM.
Front-end technologies used include HTML, CSS, and JavaScript, which contribute to the website's layout, visual representation, and interactivity.
Function requirements of the website include user mood input, mood processing, and movie recommendation generation.
Non-functional requirements focus on performance, security, scalability, usability, and maintainability of the website.
Back-end technologies include Python for programming, Pandas for data handling, NumPy for mathematical functions, and Scikit-learn for machine learning model training.
Project timeline includes planning, data collection, model development, validation, and deployment phases.
Demonstration of the user interface, showcasing the login process, mood selection, and movie recommendation features.
The system allows users to filter recommendations by mood, language, and genre, providing a tailored selection of movies.
Incorporate a search bar for users to look up specific movie titles, enhancing the user experience.
The demo concludes with a logout option, allowing for seamless user account management.
Transcripts
hello everyone I'm Mah suana and today
we'll be talking about a project which
is a mood based M recommendation
system I think everyone likes when
things are personalized according to
their taste right and especially in this
era where personalization is the key our
project aims to revolutionalize the way
we choose movies by integrating
emotional intelligence into the
recommendation process and we always
feel overwhel by all the streaming
services which are provided online right
and you unsure about what to watch so
our project basically solves that you
tell the system your modood and the
system helps you choose according to
your mod the movies it suggest you
certain movies which will be according
to the mo mod you've given input let's
say positive energetic or reflexive it
uses machine learning to recommend the
movies that match your mood it is built
using Python and flas so our Bo based
movie recommendation system it is a
cutting it solution which is designed to
understand and respond to the users's
current mood providing a tailored movie
suggestion that can enhance the user
experience in contrast with the
traditional streaming services which are
available online that uses uh the users
past history or the demographies our
project are for the purpose of our mood
based recommendation system is to
develop a system that will suggest the
content based on the user's current mood
so it uh these are the main objective
which our project focuses on the first
thing is the to enhance the user
experience so basically we are
understanding the user's mood the system
can recommend movies and content which
is more likely to be enjoyable to the
user the second thing is personalization
as I said personalization is the key in
this era so mood based movie
recommendations it provides more more
personalized experience according to
your mood than the traditional methods
and next is the content Discovery this
system helps you to explore uh or
discover new uh content which you may
not have seen before okay now so let's
talk about the stakeholders of a project
the first ones are the end users or the
movie Watchers they are directly
benefited from the recommendation system
and their satisfaction with the user
interface next ones are the software
developers the builders they basically
translate the project requirements into
a functional system it ensures technical
specifications and adares the best
practices and it is scalable for the
future grou the next ones are the
machine learning uh Engineers they train
and maintain the machine learning models
they provide accurate and more
personalized
recommendations next the next one the
next stakeholders are the project
managers project managers are basically
the coordinators which oversee the
entire development process of a project
let's say the time people required or
the budget required Etc next are the uh
streaming services the streaming
services we can say they can be a
potential collaborator in future if the
system integrates with an existing
platform then it can take it to the
different level let us look into the
hardware requirements of a project the
first we need a computer which has a
capable GPU next we'll require a
processor which is a mid-range processor
with at least four CS the next we need a
sufficient Ram which is available in our
laptops or PCS let's say 8 GB and the
and then we'll require a high
performance Hardware to accelerate the
learning model training hello everyone
I'm Julia and I'm going to explain the
front Technologies we've used in our
website so for our front end the three
main things and elements that you used
for our website include HTML CSS and
JavaScript hm has basically given us a
layout and a framework for our website
and we used different elements in it
like header main uh elements button Etc
to create an overall layout for a webite
the next element that we've used is CSS
which defines the visual representation
and presentation of our web page and
controlling the layout styling and
animation it creates a visually
appealing overall look of the website so
that it gives a modern look and touch to
our website so these are the function
requirements of our website which
include user input mode processing and
movie recommendation the first and
foremost function requirement is user
input where the user enters their mode
and we generate a movie based on their
inputs mode processing so in our website
we have different filters which
basically include a mode filter a
language filter and a genre filter where
you can select as for your requirements
and we will generate a movie based on
those requirements and we also have an
additional search bar in case you want
to search for a movie of you're liking
or choosing and the last part is movie
recommendation where we recommend you a
movie that's suitable for your mood and
customized for you using our train
trained machine learning model to
generate a movie recommendation so these
is the nonfunctional requirements of our
website which include performance
security scalability usability and
manageability performance ensures that
our system can generate good movie
recommendations quickly efficiently and
accurately and we've also implemented
robust security measures so that user
data is safe and protected our design is
also scalable so that it can handle a
growing number of users as well as
images and our website is also intuitive
and user friendly so that users can use
it with ease and maintainability we've
developed our website by with good
proper documentation so it can be
maintained and kept clean for future use
as well hello everyone this is ID and
I'll be explaining the backend
technologies that we use for
project okay so our main programming
language for this project was python we
chose python because it was like a on
stop for all our requirements and was
the most appropriate language where we
could use all our other Technologies and
models for the recommendation system and
in our recommendation model we used
fandal for data handling and then we use
num for mathematical functions in our
model recommendation we use escalar
feature extraction and metrics for
accuracy and validation and in flask we
did our user indication so using flask
we could combine our and fr to have a
function website so I'll be giving you
all a brief project timeline that we use
so our first phase is planning and
preparation and the second phase was
data collection and processing which was
done by our teammate M the third one was
model development and followed by
valuation validation of the model the
last one was deployment and
implementation of the model let me
explain in detail the first phase
planning and ption so in this phase we
basically scope out the game and
objective of our project and then we
decide who does what and we decide
theate resources needed for this project
the Second Step data collection and pre
processing so in this we used our tmb
website from kagle and we had to clean
the data and reprocess it to fit our
model followed by model development we
used skarn feature extraction and a sub
field of machine learning that is
natural language processing the model
was deployed after that using pickle
module the validation and validation we
use a scal metric accuracy and
validation methods and then the last one
is monitoring and maintenance in this
basically what we do is we update our
database and our software based on how
and how the user so let's talk a little
bit more about our development tools we
use development tools mainly for two two
phases that is our data collection and
processing and recommendation algorithms
so I like I just said our database is
from the tmtv website and we weped to
get database we use the tmtv API to get
our post links for the website and then
we use data processing librar p and
which are B Library which are used for
basically manipulating the data as for
the recommendation algorithms we use
natural language processing under
natural language processing we use the
tfid vectorizer we convert our text
based data colums into vectors using the
cosine similarity we then associate the
movie the value and the closest Value
and movies having Val similar to that
are to
the hello everyone my name is Julia pilu
and I will be showing you guys a demo on
our project the Moon movie website let
me Begin by presenting my
screen okay so this is our landing page
or our homepage
and this is our footer section along
with our service
cards so let's get
started we get started by clicking on
that
button and if you are an existing user
you can log in and if you are a new user
you'll have to sign up since I already
have an account made let me log in
and let me enter my
password
login so this is my user
profile where I can get my movie
recommendations generated based on my
mode so this is the core functionality
of our website and it has filters like
movie name JRA language and mode let me
Begin by selecting a
mode let's say relax
in and I click on the recommend button
and it gives me a list of movie
recommendations for my mood that's
relaxing and what it basically does is
it gives you a movie poster along with
the movie title and a short description
about the
movie let's check out some other
filters so let's say I want to watch a
Hindi movie
I click on the Hindi language filter and
let's suppose my mode is
neutral and let me
recommend so these are the movies that
it recommends for me where I can watch
them whenever I want in whatever mode
they're all classic
movies so let me go
back and you don't have to select both
filters at the same time you can only
select one filter for example over here
I've selected only the language filter
and I've made it so that the language is
Spanish so it recommends a list of
Spanish movies for
me and I can pick whichever one I want
among these and watch
them I can also select based on um genre
so let's say comedy
and it recommends a mixture of all
comedy movies for me for a variety of
languages it's not just limited to
English
movies and suppose I want to look up a
movie based on its title let's say
rashar and it gives you a list of all
the rashar movies in sequence it gives
you the movies title and the
description in case I want to know which
rashar movie to watch
again so yeah this is our movie
recommendation machine learning
model and now I can go back and log
out and it takes me back to our homepage
where another user can sign up login or
I can repeat the same process
again so this was it for the demo thank
you guys so much for listening to me
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