Popular GitHub Repositories for Movie Recommendation

The purpose of our research is to study reinforcement learning approaches to building a movie recommender system. We formulate the problem of interactive recommendation as a contextual multi-armed bandit.

Content based movie recommendation system with sentiment analysis

使用MovieLens数据集实现了基于Auto Encoder(AE), Variational Auto Encoder(VAE), BERT的深度学习电影推荐系统

Movie Recommendation Chatbot provides information about a movie like plot, genre, revenue, budget, imdb rating, imdb links, etc. The model was trained with Kaggle’s movies metadata dataset. To give a recommendation of similar movies, Cosine Similarity and TFID vectorizer were used. Slack API was used to provide a Front End for the chatbot. IBM Watson was used to link the Python code for Natural Language Processing with the front end hosted on Slack API. Libraries like nltk, sklearn, pandas and nlp were used to perform Natural Language Processing and cater to user queries and responses.

? Movie Recommender AI System

Movie recommendation system based on hybrid recommender and clustering

In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.

Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows

A Hybrid Recommendation model based on sentiment analysis on tweets and item based filtering to closely match preferred recommendation.

An item-based recommender model that computes cosine similarity for each item pairs using the item factors matrix generated by Spark MLlib’s ALS algorithm and recommends top 5 items based on the selected item.

A Deep-learning-based context-aware recommendation system with two kinds of attention modules

The movie recommendation chatbot built with Rasa is an intelligent conversational agent designed to assist users in discovering movies tailored to their preferences and interests.

This project combines the power of Google's Gemini Pro language model with Langchain Agents to create an intelligent movie recommender system.

Machine Learning based project to recommend movie

MovieBot. A Movie Recommendation Bot built with Flask, HTML CSS, and Cosine Similarity ML Model!

Watch It Now: Explore trending movies/TV shows via TMDb API, get personalized suggestions from Palm 2, and enjoy secure access with user authentication and Firestore database integration.

Repository for the university course AI and the Web

Winning movie-recommending chatbot at Oath/Verizon Chatbot Hackathon

Movie finder app with semantic search and sentiment analysis