Combines user-based and item-based recommendation systems to deliver personalized movie suggestions, utilizing user preferences and film characteristics.
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Apr 1, 2024 - Python
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user-based-recommendation
Here are 17 public repositories matching this topic...
Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
spark collaborative-filtering xgboost cosine-similarity upsampling jaccard-similarity tfidf content-based-recommendation surprise-python pearson-correlation xgboost-regression tf-idf-score svd-matrix-factorisation user-based-recommendation item-based-recommendation similar-items feature-augmentation
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Jun 3, 2021 - Python
Association Rule Learning, Content Based Recommendation, Item Based Collaborative, Filtering User Based Collaborative Filtering, Model Based Matrix Factorization projects i've done about
python matrix-factorization recommendation-system arl content-based-recommendation user-based-recommendation item-based-recommendation
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Jan 25, 2023 - Python
User-based collaborative filtering movie recommender using MovieLens dataset
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Mar 4, 2020 - Python
An application that recommends music on the basis of previous heard songs of a user using a ML model. Using Collaborative-based filtering to recommend other songs similar to what the user likes. Download Data set from Kaggle (Million song data set)
collaborative-filtering recommendation-system python-flask-application user-based-recommendation music-recommendation-system
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Jan 8, 2023 - Python
Recommendation algorithms
python algorithm tensorflow word2vec pytorch collaborative-filtering recommendation-system recommender-system knn google-translate konlpy annoy bert-model user-based-recommendation item-based-recommendation huggingface-transformers mean-based-recommendation
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Nov 13, 2023 - Python
In this repository, I implement a recommender system using matrix factorization. Here, two types of RS are implemented. First, use the factorized matrix for user and item. and second, rebuild the Adjacency matrix. both approaches are acceptable and implemented in this repo. To factorized the matrix, funk-svd Algorithm is used. you can find his i…
matrix-factorization recommender-system svd funk-svd user-based-recommendation item-based-recommendation factorized-matrix
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Jan 12, 2021 - Python
A simple movie recommender system that uses two main approaches to make recommendations: Content-based algorithm and Collaborative filtering algorithm (User-based).
collaborative-filtering recommendation-system recommender-system content-based-recommendation content-based-filtering user-based-recommendation
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Feb 24, 2021 - Python
A study on the naive user-based collaborative filtering algorithm and related improvements on the Movielens dataset.
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Dec 16, 2020 - Python
An application that uses the algorithm of user-based collaborative filtering and item-based collaborative filtering to recommend new movies
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Nov 29, 2020 - Python
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
matrix-factorization recommender-systems implicit-feedback hybrid-recommender-system content-based-filtering user-based-recommendation
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May 14, 2024 - Python
A python implementation of a hybrid semantic-based collaborative filtering recommender systems.
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Mar 14, 2023 - Python
The site offers movie recommendations based on user and item-based collaborative filtering, utilizing other users' ratings to provide personalized suggestions on the website.
javascript mysql css python html django sql cosine-similarity knn user-based-recommendation item-based-recommendation movie-recomendation-system
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Oct 2, 2023 - Python
collaborative filtering project was developed using surprise library. It provides user based and item based search. It calculates similarity score and offers suggestions.
python collaborative-filtering surprise-python surprise reccomendersystem user-based-recommendation item-based-recommendation reccomendation-system
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May 17, 2025 - Python
This project analyzes a movie dataset using machine learning algorithms to predict success, explore revenue-popularity relationships, and develop recommendation systems. It employs techniques like K-Means, DBSCAN, GMM, decision trees, PCA, and NLP for insights and personalized suggestions.
python nlp machine-learning natural-language-processing clustering scikit-learn data-visualization pca recommendation-system data-analysis k-means predictive-modeling gmm decision-tree content-based-recommendation user-based-recommendation
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Nov 30, 2024 - Python
Demo is available at https://huggingface.co/spaces/quyanh/Book-Recommender-System
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May 26, 2023 - Python
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