Popularity-Based and Collaborative Filtering based Restaurant Recommender System

The creation of a Popularity-based and Collaborative Filtering Based Restaurant Recommendation System is the main focus of this project, which is an important application in the fields of recommendation systems and machine learning. The system uses the different python libraries like scikit-learn, pandas, NumPy, Linear Regression, Hashlib and Matplotlib as well as technologies like Python and the Flask Framework, to recommend the restaurants to the users based on the ratings out of 5 and their tastes. With features including user registration, login, restaurant browsing, and recommendation, the suggested system is a user-centric program. The system offers a smooth user experience by combining backend Python frameworks with frontend technologies like HTML, CSS, and Bootstrap. A thorough analysis of the literature made it clear that recommendation systems are essential for improving user pleasure and experience across a range of industries. But my cutting-edge restaurant suggestion system offers a fresh approach. My solution combines cutting-edge machine learning algorithms with intelligent analysis of customer preferences to deliver customized restaurant recommendations. My system uses collaborative and popularity-based filtering algorithms to make sure that users receive recommendations based on their interests and preferences. Through the integration of data from many sources, such as restaurant attributes and user ratings, My system generates precise and pertinent recommendations that boost user engagement and pleasure. Furthermore, My platform offers a user-friendly experience with little additional infrastructure requirements by seamlessly integrating with current websites and applications. My goal is to revolutionize the restaurant discovery process and improve the eating experience for people globally by promoting the widespread adoption of My technology through smart relationships with industry players.


Introduction
When it comes to eating experiences, customers looking for customized suggestions have a significant hurdle when sorting through the multitude of restaurant selections.My creative restaurant recommendation system seeks to successfully handle this problem by utilizing collaborative filtering and Popularity based methods and machine learning capabilities.Through the application of sophisticated algorithms, My technology is particularly good at determining user preferences and making customized recommendations, which improves the dining experience for people in a variety of culinary contexts.
The underlying principle of My system is to comprehend user behavior and preferences in order to generate intelligent recommendations that are predicated on previous interactions and preferences.My software creates individualized suggestions by evaluating past data, restaurant features, and user ratings to maximize customer pleasure and engagement during the restaurant choosing process.I adopted a Popularity-based method for my project.I utilized one Kaggle dataset entitled zomato.csv,which has approximately 56117 data points.So, what I did was read the dataset with pandas and then preprocessed some data in jupyter notebook.Then I deleted all of the duplicate values from the dataset and filled in the null location with 0. After that, I did popularity-based filtering.Popularity-based filtering is simply a method or type that displays the top material available on the platform and recommends the same top content to all users.Examples include YouTube's trending page and IMDB's 250 movies.I used the Flask application to display the top 50 restaurants based on their average rating, including name, image, cost, votes, location, rating, and link.Below the top 50 restaurants, I've provided a linear regression and a bar regression graph based on ratings and costs.And for this, I utilized the WR formula.I employed the collaborative filtering approach in my project.
I recommend restaurants to users based on their ratings.My collaborative filtering method involves collecting user ratings out of 5 for each of 56117 restaurants and approximately 1000 people.So collaborative based filtering treats each restaurant as a twodimensional point on the x axis (assume user1) and the y axis (user2), and shows the restaurants in that space.So, after arranging the restaurants on that two-dimensional space, it takes the one plotted restaurant and finds the nearest distance from it to other plotted restaurants, so basically it calculates the Euclidian distance between each plotted restaurant and It then recommends restaurants based on their proximity to the user's search criteria.So, it uses cosine similarity to propose the eatery.I also used the flask application to deploy on the website, so when the user goes to the recommend menu and enters one of the restaurants in his city, I present the 5 recommendations with the image name, cost, location, rating link, and 5-star symbol rating, allowing the user to review the restaurant.For this, I filtered the data as follows: I examined only those users who rated a minimum of 200 restaurants, and I considered only those restaurants where a minimum of 50 users rated them.Based on these, I made a recommendation to the user based on these criteria.I've also given the linear regression graph for the recommended restaurants, as well as the bar regression graph, to help the user understand better.Furthermore, I guarantee that My system interacts with current platforms and apps without any problems, providing customers with a simplified experience that requires the least amount of extra infrastructure.This is part of My dedication to seamless integration.My technology, which is compatible with several platforms and flexible, has the potential to transform how people find and explore restaurants, improving their overall dining experiences.

Motivation
My initiative is motivated by the restaurant industry's increasing desire for personalized recommendations.As consumers seek bespoke experiences that match their preferences and dietary constraints, there is a demand for creative solutions that can navigate the huge number of eating alternatives and make relevant recommendations.Traditional methods of restaurant discovery sometimes fall short of providing individualized recommendations, leaving users overwhelmed by choice and uncertainty.My method aims to bridge this gap by leveraging machine learning and collaborative filtering to provide accurate and relevant restaurant suggestions.Our solution attempts to streamline the restaurant selection process by learning user preferences and behavior, increasing user pleasure and engagement with the dining experience.

Need of Study
The need of research in this domain arises due to various problems occurred which are as follows : 1. Enhanced User Experience: My system attempts to improve the user experience by making personalized restaurant recommendations based on individual tastes and dietary constraints.I hope to expedite the restaurant discovery process and relieve users of decision fatigue by utilizing machine learning techniques.

Improved Engagement:
Studies have demonstrated that personalized recommendations boost user engagement and satisfaction.My approach aims to increase user engagement and loyalty on the restaurant discovery platform by providing appropriate suggestions based on user preferences.

Enhanced accessible: Traditional methods of restaurant discovery frequently lack accessible elements
for persons with specific dietary requirements or tastes.My approach aims to address this issue by making individualized recommendations that cater to a wide range of user needs and tastes.4. Time Efficiency: Due to the abundance of dining options accessible, users frequently spend substantial time searching for restaurants that match their interests.My technology seeks to save consumers time by providing personalized recommendations, improving the restaurant discovery process and increasing time efficiency.5. Scalability: As the restaurant business evolves, there is an increasing demand for scalable systems that can respond to changing user preferences and market trends.My system is meant to be scalable, allowing for smooth integration with existing platforms and applications, as well as future growth to handle larger user bases and restaurant databases.
Overall, My System addresses critical needs in the restaurant discovery process, including enhanced user experience, improved engagement, accessibility, time efficiency, and scalability, making it a valuable asset for users and stakeholders in the restaurant industry.

Literature Survey
There is a corresponding need to address issues in the field of personalized recommendation systems, particularly in the restaurant industry.By reviewing relevant studies, we can obtain insights into the methodology and technology used in related areas, which will inform the development This study looks into the function of big data analytics in making personalized restaurant suggestions to users.The system generates specialized suggestions by combining enormous amounts of data from many sources, such as user preferences, historical patterns, and restaurant qualities, using advanced analytics techniques.The study underlines the scalability and versatility of big data-driven approaches for meeting the changing demands and preferences of restaurant customers.This study investigates the increasing trend of context-aware restaurant recommendation systems, which take into account environmental characteristics as well as user preferences.Traditional recommendation systems concentrate primarily on user behavior and preferences, ignoring the impact of extrinsic variables such as location, weather, and time of day.By incorporating contextual information into the recommendation process, these systems hope to provide consumers with more relevant and personalized options.The study emphasizes the role of context in influencing dining preferences and experiences.For example, a user may prefer to dine outdoors on a sunny day rather than indoors during rainy weather.Similarly, the time of day can determine whether a user wants a quick coffee break or a relaxing meal.By adding environmental information into the recommendation process, context-aware algorithms can adapt suggestions to better match user preferences and situational demands. [06] All the techniques related to the subject have their own advantages and disadvantages.This paper uses Latest technologies and advancements in Machine Learning, Data Science, etc.My technology provides every user top 50 restaurant with highest ratings and recommend the restaurant based on users' Favorite cuisines and the rating which is given by more than 200 users.

III. Methodology Fig 2. Activity Diagram
In this section, we outline the methodology and technology stack employed in the development of a Popularity-Based and Collaborative Filtering Based Restaurant Recommender System uses the Machine Learning algorithm to recommend the restaurants to users based on rating out of 5 which given by more than > 200 users and on more than > 50 restaurants.This methodology consists the following :  To determine the popularity of each restaurant, I have calculate the average rating by aggregating all user ratings and dividing by the total number of ratings.The formula to calculate the average rating AvgRating for a restaurant is given by: Where Ratingi represents the rating given by the ith user, and n is the total number of ratings for the restaurant.

b) Selection of Top-Rated Restaurants:
Based on the calculated average ratings, I have selected the top-rated restaurants to recommend to users.Restaurants with higher average ratings are considered more popular and are prioritized in the recommendation list.To show the top 50 restaurants from my platform I have used the Weighted rating formula:-

c) Presentation and Visualization:
The top-rated restaurants are prominently displayed on the website, providing users with easy access to popular dining options.Each recommended restaurant is accompanied by relevant details such as name, image, cost, location, and rating, enhancing user engagement and decision-making.Collaborative filtering (CF) is a widely used technique for recommendation systems that leverages useritem interactions to generate recommendations.One of the seminal works in this field is the paper by Koren, Bell, and Volinsky [1], which introduced matrix factorization techniques for CF.The authors proposed the use of low-rank matrix factorization to model user-item interactions, allowing for the prediction of missing values in the user-item matrix.This approach has been widely adopted due to its effectiveness in capturing latent factors underlying user preferences.
Collaborative filtering is a powerful technique that I have used in my system to recommend restaurants to users based on their past ratings and interactions.Here's a detailed explanation of how collaborative filtering works in My system: a) Representation as Two-Dimensional Space: Collaborative filtering treats each restaurant as a point in a two-dimensional space, with one axis representing the ratings given by User 1 and the other axis representing the ratings given by User 2. By plotting all restaurants in this space, you create a visualization where the proximity of restaurants indicates similarity in user ratings.

b) Calculation of Similarity:
To determine similarity between restaurants, you calculate the Euclidean distance between each pair of restaurants in the two-dimensional space.The Euclidean distance formula between two points (X1,Y1) and (X2,Y2) is given by: This distance measure helps identify restaurants that are close to each other in terms of user ratings.c) Nearest Neighbor Selection: Once distances are calculated, you select the nearest neighboring restaurants for each restaurant based on a predefined threshold or number of neighbors.These nearest neighbors represent restaurants with similar user ratings and preferences.

d) Recommendation Generation:
To generate recommendations, you use the cosine similarity metric to compare the ratings of the target restaurant with its nearest neighbors.The cosine similarity between two vectors is given by: Recommendations are made based on the nearest neighbor restaurants with the highest cosine similarity scores.

e) Filtering Criteria:
To ensure the quality of recommendations, you filter out users who have rated fewer than 200 restaurants and restaurants that have been rated by fewer than 50 users.This filtering ensures that recommendations are based on a sufficient volume of data.

f) Presentation and Visualization:
Recommendations are presented to users through a Flask-based web application, where they can input a restaurant name from their city.The application displays the top 5 recommendations along with relevant details such as image, name, cost, location, rating, and a 5-star symbol for user feedback.Additionally, linear regression and bar regression graphs are shown to provide users with insights into the recommended restaurants' characteristics.

Database Management
SQLite database is used to store user information securely, including usernames, passwords, and preferences.Database operations include user registration, authentication, and storing/retrieving restaurant data.Proper database management ensures data integrity and efficient data access for the web application.

Testing and Validation
The web application is thoroughly tested for functionality, including user registration, authentication, recommendation generation, and contact form submission. Model evaluation is conducted to assess the performance of collaborative filtering using metrics such as MAE and RMSE.Feedback from users is gathered to iteratively improve the application.

Documentation and Maintenance
Comprehensive documentation is provided, including project architecture, functionalities, algorithms used, and deployment process.Instructions for users on how to use the application are included.Regular maintenance and updates are performed to address bugs, improve performance, and incorporate user feedback, ensuring the continued effectiveness and relevance of the application

SYSTEM DESIGN Aim
My System's main goal is to create a reliable restaurant recommendation system that makes efficient use of machine learning algorithms to improve user experience and optimize restaurant choices.This system combines collaborative and popularity-based filtering techniques to deliver personalized suggestions based on user preferences.My objective is to enhance customer pleasure and boost platform engagement by tackling this goal.Objectives 1. Personalized Recommendations: The system aims to provide personalized restaurant recommendations tailored to individual user preferences, enhancing the overall user experience.

Integration of Multiple Filtering Techniques: By incorporating both popularity-based and
collaborative filtering approaches, the system seeks to leverage the strengths of each method to generate accurate and diverse recommendations.

Real-time Adaptability:
The system must be capable of adapting to changing user preferences and restaurant data in real-time, ensuring that recommendations remain relevant and up-to-date.4. Enhanced User Engagement: By presenting users with relevant and appealing restaurant recommendations, the system aims to increase user engagement and retention on the platform.

Optimized Performance:
The system must deliver recommendations efficiently, minimizing response times and ensuring a seamless user experience.

Scalability and Flexibility:
The system should be scalable to accommodate a growing user base and flexible enough to incorporate new features and functionalities in the future.7. Data Privacy and Security: Ensuring the privacy and security of user data is paramount.The system must implement robust measures to protect user information and adhere to data protection regulations.

Problem Statement
The main goal of this project's issue statement is to create an intelligent recommendation system that will help users choose restaurants more easily.Current recommendation systems often don't personalize enough or don't adapt well enough to different user preferences.By employing machine learning algorithms to examine user behavior and preferences and produce customized recommendations, my proposed solution solves this issue.We anticipate that by resolving this problem, user retention and platform engagement would increase, leading to increased business growth.
In order to solve this problem, my suggested method combines collaborative and popularity-based filtering algorithms to offer customized restaurant recommendations that are in line with user tastes and behavior.By resolving this problem, we intend to raise user engagement and satisfaction levels and, eventually, promote revenue growth for restaurateurs.

TECHNOLOGIES USED
The restaurant recommendation system uses a range of technologies to collect data, process user preferences, and provide personalized recommendations.Each technology contributes significantly to the system's performance and usability.The following are the technologies used in the project:

Dataset Acquisition and Processing
The Zomato dataset obtained from Kaggle serves as the primary data source for the restaurant recommendation engine.This dataset provides information about thousands of restaurants, such as their names, locations, cuisines, ratings, and other pertinent features.The dataset serves as the foundation for building machine learning models and making suggestions based on user preferences.

Machine Learning Algorithms
As a branch of artificial intelligence (AI), machine learning (ML) aims to create models and algorithms that can learn from data and produce conclusions or predictions without the need for explicit programming instructions.In order to provide personalized recommendations, machine learning analyzes past interactions, institution data, and client preferences.This is a critical function of machine learning in restaurant recommendation systems.The following is a list of the key components and principles of machine learning that apply to the project.a. Supervised Learning: In supervised learning, a model is trained using labeled data, with each data point representing a target label or outcome.In the restaurant recommendation system, supervised learning techniques are utilized to anticipate customer preferences or restaurant ratings based on previous data.b.Unsupervised Learning: Unsupervised learning involves training a model on unlabeled data to discover patterns, structures, or groupings within the data.In the context of the recommendation system, unsupervised learning techniques may be used for clustering similar restaurants or identifying user segments based on preferences.c.Collaborative Filtering: Collaborative filtering is a popular technique used for recommendation systems, where similarities between users or items are leveraged to make predictions or recommendations.In the restaurant recommendation system, collaborative filtering algorithms analyze user ratings or interactions with restaurants to identify similar users or restaurants and make personalized recommendations.d.Popularity-Based Filtering: Popularity-based filtering is a simple yet effective approach where recommendations are based on the overall popularity or average ratings of items.In the recommendation system, popularity-based filtering may be used to recommend top-rated or trending restaurants to users.e. Linear Regression: The relationship between a dependent variable and one or more independent variables can be statistically represented using linear regression.The relationship between restaurant variables (such as pricing and location) and user ratings can be investigated in the context of the recommendation system using linear regression, potentially providing insights for recommendation development.

Flask Framework
Developing online applications and APIs is made simple with Flask, a lightweight and flexible Python web framework.Flask can be used for projects of different sizes and complexity since it provides a set of straightforward but effective web application development tools.User interaction with the restaurant recommendation system is facilitated by the web application interface created with Flask framework.
Relevant to this project, the following are significant Flask features and components: a. Routing: Flask uses routes to map URLs to functions, allowing developers to define endpoints for handling requests from clients.Routes specify the URL pattern and the corresponding function to execute when the pattern matches a request.b.Templates: Flask supports the use of Jinja2 templates for generating dynamic HTML content.
Templates allow developers to create reusable HTML templates with placeholders for dynamic data, making it easy to generate dynamic web pages.c.Request Handling: Flask provides built-in functions for handling various types of HTTP requests, including GET, POST, PUT, and DELETE.Developers can define route functions to handle different types of requests and access request data such as form inputs or URL parameters.d.Response Generation: Flask enables the generation of HTTP responses using built-in functions or custom response objects.Developers can return HTML content, JSON data, or other types of responses based on the client's request.e. Extension Ecosystem: Flask has a rich ecosystem of extensions that extend its functionality and provide additional features.Extensions cover areas such as authentication, database integration, form handling, and more, allowing developers to add advanced functionality to their Flask applications.

HTML, CSS, and Bootstrap
The online application's user interface is designed and made using HTML, CSS, and Bootstrap.The creation of aesthetically pleasing and intuitive user interfaces is made possible by these technologies, which improves the user experience as a whole.Web pages' content is organized using HTML, styled and laid out using CSS, and responsive design elements and pre-designed components are provided by Bootstrap.

SQLite Database
The database management system utilized by the recommendation system to store user data, restaurant data, and other pertinent data is SQLite.It provides a quick and effective way to manage data in the web application, guaranteeing quick access to and retrieval of data.User preferences, ratings, and other data that are necessary for creating recommendations can be stored and retrieved more easily with SQLite.

Conclusion
To summarize, the development of the Popularity-Based and Collaborative Filtering-based Restaurant Recommendation System is a big step forward in the use of machine learning algorithms to improve user dining experiences.The system promises to give personalized restaurant suggestions based on individual interests and user behavior by combining popularity-based and collaborative filtering methodologies.The system's architecture uses complex AI algorithms to assess user ratings, restaurant features, and geographical data in order to create appropriate suggestions.Popularity-based filtering displays the highest-rated eateries, whereas collaborative filtering uses user ratings and preferences to provide individualized recommendations based on similarity criteria.Moving forward, the project has enormous potential for future improvements and extensions.Integration with developing technologies, including augmented reality, dynamic pricing techniques, and social media integration.Furthermore, attempts to improve data privacy, expand into new areas, and continuously develop based on user feedback will be critical for the system's relevance and competitiveness in the ever-changing field of restaurant recommendation systems.Overall, the Popularity-Based and Collaborative Filtering-Based Restaurant Recommendation System demonstrates the ability of machine learning and artificial intelligence in transforming how people discover and interact with restaurants.By using the richness of available data and smart algorithms, the system not only aids informed decision-making but also fosters a more delightful and personalized dining experience for users worldwide.
[04] Title: Future Directions in Restaurant Recommendation Systems: A Roadmap Publishing Year: July 2022 This forward-looking study examines future directions and developing trends in restaurant recommendation systems.The study looks at current advances in machine learning, artificial intelligence, and data analytics to identify possible areas for innovation and improvement.The report sees a future in which restaurant recommendation systems use cutting-edge technology to provide users with seamless and tailored dining experiences around the world.[05]Title: Context-Aware Restaurant Recommendation Systems: Integrating Environmental Factors Publishing Year: October 2021

Fig 11 .
Fig 11.DFD Level 2 Diagram of Restaurant Recommendation System