A recommendation system for anime using MyAnimeList Dataset provided by their API
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Updated
Aug 18, 2024 - Jupyter Notebook
A recommendation system for anime using MyAnimeList Dataset provided by their API
A content-based movie recommendation model using NLP techniques to analyze and suggest movies based on metadata like genres, keywords, and plot summaries.
Hybrid recommender system - Collaborative filtering + Content based filtering (same as used by Netflix).
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A content-based system recommends movies to an input user based on the weighted genre score of a movie and a collaborative filtering system recommends movies liked by other users having a similar taste profile as the input user.
Evaluations and Comparisons of Recommendation Systems Using The MovieLens Dataset
Trend Fitness is a web application dedicated to providing professional fitness advice which will include a range from fitness plans to diet plans catered to every individual needs. I believe that my web application will embark on a transformative journey towards a healthier lifestyle.
Unleash your audio muse: AI-powered music recommendations with Spotify and Streamlit. Discover new favorites based on what you love.
Collaborative and hybrid recommendation systems
recommending recipes with content-based filtering approach
A simple Product Recommendation System.
The purpose of this project is to predict student loan repayment success using a neural network. Neural networks are computational models inspired by the human brain's structure and function, consisting of layers of interconnected nodes or "neurons" that can learn to recognize patterns in data.
The purpose of this project is to develop a recommender system based on content-based filtering in the Python programming language.
A movie recommendation system using IMDb's weighted ratings and custom filters.
BrewMaster's is a website aimed at simplifying coffee shop customer’s decision-making process in choosing which coffee to order according to their preferences.
Build a personalized Music Recommendation System using Spotify API and Python. The system uses content-based and hybrid filtering to suggest songs based on user preferences, enhancing the music discovery experience.
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
Restaurant Recommendation Application
Transforming skincare recommendations: our hybrid system combines KNN, CNN, and EfficientNet B0 for personalized advice. Published in IEEE, with 80% validation accuracy and 87.10% training accuracy.
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