Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows
-
Updated
Sep 5, 2019 - HTML
Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows
The sample code repository leverages Azure Text Analytics to extract key phrases from the product description as additional product features. And perform text relationship analysis with TF-IDF vectorization and Cosine Similarity for product recommendation.
EDA, Pre-processing, 6 Recommendation Systems Techniques: * Popularity-Based, * Cosine Similarity Collaborative Filtering, * Matrix Factorization Collaborative Filtering, * Clustering, * Content-Based Filtering, * Hybrid Recommendation System.
Recommender Application for Programming Languages, Projects, and Publication Paper using Content-Based Recommendation System and KeyBERT
I have created a book recommender system that recommends similar books to the reader based on his/her interest. This project shows results of collaborative and content-based filtering of the given dataset.
NextRead is a book recommender system built for Book Lovers. Simply enter your current favourite book and get peronalized book list to find your new favourite.
Add a description, image, and links to the content-based-filtering topic page so that developers can more easily learn about it.
To associate your repository with the content-based-filtering topic, visit your repo's landing page and select "manage topics."