Enhancing Restaurant Recommendations through User-Based Collaborative Filtering

Sophan, Moch Kautsar; Santoso, Iwan; Permana, Kurniawan Eka

doi:10.1109/icic60109.2023.10381923

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…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.…”

Section: C) Presentation and Visualizationmentioning

confidence: 99%

Popularity-based and Collaborative Filtering Based Restaurant Recommender System

2024

IJFMR

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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.

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Section: C) Presentation and Visualizationmentioning

confidence: 99%

Popularity-based and Collaborative Filtering Based Restaurant Recommender System

2024

IJFMR

View full text Add to dashboard Cite

show abstract

Enhancing Restaurant Recommendations through User-Based Collaborative Filtering

Cited by 1 publication

References 21 publications

Popularity-based and Collaborative Filtering Based Restaurant Recommender System

Popularity-based and Collaborative Filtering Based Restaurant Recommender System

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