Popularity-based and Collaborative Filtering Based Restaurant Recommender System

Nishant Wale

doi:10.36948/ijfmr.2024.v06i02.15985

Popularity-based and Collaborative Filtering Based Restaurant Recommender System

Author(s)	Nishant Wale
Country	India
Abstract	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.
Keywords	Restaurant Recommendation, Machine Learning, Popularity-Based Filtering, Collaborative Filtering, User Preferences, Personalization, Integration, User Engagement, Linear Regression.
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-04-01
Cite This	Popularity-based and Collaborative Filtering Based Restaurant Recommender System - Nishant Wale - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.15985
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.15985
Short DOI	https://doi.org/gtpw8q

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Popularity-based and Collaborative Filtering Based Restaurant Recommender System

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