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This is a movie recommendation system with tensorflow. Dataset is MovieLens.

A general movie recommending system involving python, numpy, sklearn, pandas. Applied feature-based modeling, content-based filtering, collaborative filtering and clustering method. Provided hybrid online/offline movie recommendations for anonymous users, new users (cold-start) and old users.

Implementation of movie recommendation system using C

Movie Recommendation System using the MovieLens dataset

No description available from source.

Implemented Content-based filtering, Collaborative filtering and K-Means Clustering on MovieLens Dataset(https://www.kaggle.com/rounakbanik/the-movies-dataset/data)

电影推荐系统

Making movies recommendation using a Collaborative Filtering Algorithm on the famous MovieLens dataset.

Content Based Recommendation system uses attributes of the content to recommend similar content. It doesn't have a cold-start problem because it works through attributes or tags of the content, such as actors, genres or directors, so that new movies can be recommended right away.

🍿 Never say "I don't know what to watch" again! NeuroFlix is your personal film sommelier. This interactive app recommends movies you'll love based on the films you already like. Just pick a title and get a curated list of similar movies, all powered by machine learning magic (cosine similarity o... Read more