Python Movie-recommendation Resources

Stream-Cli stream-cli is a Python scrapping CLI that combine scrapy and webtorrent in one command for streaming movies from your terminal. Installatio

Torch-RecHub A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend. 安装 pip install torch-rechub 主要特性 scikit-learn风格易用

Stream-Cli stream-cli is a Python scrapping CLI that combine scrapy and webtorrent in one command for streaming movies from your terminal. Installatio

Smooth ReLU in PyTorch Unofficial PyTorch reimplementation of the Smooth ReLU (SmeLU) activation function proposed in the paper Real World Large Scale

CTR Algorithm 根据论文, 博客, 知乎等方式学习一些CTR相关的算法 理解原理并自己动手来实现一遍 pytorch & tf2.0 保持一颗学徒的心！ Schedule Model pytorch tensorflow2.0 paper LR ✔️ ✔️ \ FM ✔️ ✔️ Fac

OD-Rec Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation' Paper, saved teacher models and Andro

Session-aware BERT4Rec Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 shor

CORE This is the official PyTorch implementation for the paper: Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao. CORE: Simple and Effective Sess

This is our implementation for the paper: Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation Xiaokun Zhang, Bo

Intention Adaptive Graph Neural Network (IAGNN) This is the official repository of paper Intention Adaptive Graph Neural Network for Category-Aware Se

Product-based-recommendation-system A product based recommendation system which

Welcome to notflix 👋 This is a project to help organise shows to watch with my

Ontoseer This document provides documentation for the first version of OntoSeer.OntoSeer is a tool that monitors the ontology development process andp

A python scripts that uses 3 different feature extraction methods such as SIFT, SURF and ORB to find a book in a video clip and project trailer of a movie based on that book, on to it.

Recommender systems are the systems that are designed to recommend things to the user based on many different factors. The recommender system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest.

Stream-Cli application that allow you to play your favorite movies from the terminal

Amazon Recommendation Review Dataset Analyzer A notebook to analyze Amazon Recommendation Review Dataset. Features Calculates distinct user count, dis

YTSP It is a CLI movie streaming client which works on yts.mx API written in pyt

This project is based on cloud services to create data lake, ETL process, train and deploy learning model to implement a recommendation system.

plex-recommender Description: Get movie/tv recommendations based on your current

News-Recommendation-system-using-Bert4Rec-model Bert4rec for news Recommendation

Movies-Scraper You are probably tired of navigating through a movie website to get the right movie you'd want to watch during the weekend. There may e

Movie Recommendation 🍿 System Watch Tutorial for this project Source IMDB Movie 5000 Dataset Inspired from this original repository. Features Simple

Ok Mugle! 🎵 장르부터 멜로디까지, Content-based Music Recommendation 'Ok Mugle!'은 제13회 투빅스 컨퍼런스(2022.01.15)에서 진행한 음악 추천 프로젝트입니다. Description 📖 본 프로젝트에서는 Kakao

Respiratory-Health-Recommendation-System Respiratory Health Recommendation System based on Air Quality Index Forecasts This project aims to provide pr

Laporan Proyek Machine Learning - Azhar Rizki Zulma Project Overview Domain proyek yang dipilih dalam proyek machine learning ini adalah mengenai hibu

Recommendation-Systems-for-IBM-Watson-Studio-platform Project Overview In this project, I analyze the interactions that users have with articles on th

Crab - A Python Library for Recommendation Engines

Sentiment Analyzer The goal of this project is to perform sentiment analysis on textual data that people generally post on websites like social networ

IMDB Success Predictor Project involves Web Scraping custom IMDB data between 2020 and 2021 of 10000 movies and shows sorted by number of votes ,fine

REST The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. Usage Download dataset Download

Sentiment Based Product Recommendation System The e-commerce business is quite p

Book My Show PC What is this? Movie Ticket Reservation App made with Tkinter. Tk

I label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive. Obstacles like sentence negation, sarcasm, terseness, language ambiguity, and many others make this task very challenging.

Sentiment-Analysis and EDA on the IMDB Movie Review Dataset The main part of the work focuses on the exploration and study of different approaches whi

Python3-based Douban Movie/Douban Book Scarpy crawler for cover downloading + data crawling + review entry.

SUCP Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation () Direct Friends (i.e., users who follow each o

2021 credit card consuming recommendation

DeepCTR DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can

FlatGCN This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation, submitted to ICASSP2022). Req

SubAdBlock Adblocker for movie subtitles. Usage Place "main.py" and "config.conf" in directory with subtitles and run "main.py". It will automatically

Content-Based-Movie-Recommender-System This model relies on the similarity of the items being recommended. (I have used Pandas and Numpy. However othe

Movie-Recommender-System Movie-Recommender-System is a web application using which a user can select his/her watched movie from list and system will r

OMDB-and-TasteDive-Mashup This hadns-on project is in the Python 3 Programming Specialization offered by University of Michigan via Coursera. Mashing

A scalable on-line movie recommender using Spark and Flask This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens datase

Book Recommendation System A recommendation system for suggesting new books given similar books. Datasets Dataset Kaggle Dataset Notebooks goodreads-E

recommendationsystem matrixfactorization colloborative filtering recommendation

動機 face_recognitionを使用して得られる顔座標は長方形であり、この座標をそのまま用いてぼかし処理を行った場合得られる画像は醜い。 それに対してモ

Introduction QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and

Perplex Perplex is a Discord Rich Presence implementation for Plex. Features Modern and beautiful Rich Presence for both movies and TV shows The Movie

G-Bert Pre-training of Graph Augmented Transformers for Medication Recommendation Intro G-Bert combined the power of Graph Neural Networks and BERT (B

Demo run: The below video will highlight the runtime of this setup and some sample real-time conversations using the power of RASA + TigerGraph, Steps

Movies-ETL ETL pipeline on movie data using Python and postgreSQL Overview This project consisted on a automated Extraction, Transformation and Load p

Books-Recommendation Business Problem During the last few decades, with the rise

IMDB Sentiment Analysis This is the final project of Machine Learning Courses in Huazhong University of Science and Technology, School of Artificial I

DeepRec Introduction DeepRec is a recommendation engine based on TensorFlow 1.15, Intel-TensorFlow and NVIDIA-TensorFlow. Background Sparse model is a

RecoEdge: Bringing Recommendations to the Edge A one stop solution to build your recommendation models, train them and, deploy them in a privacy prese

mnamer mnamer (media renamer) is an intelligent and highly configurable media organization utility. It parses media filenames for metadata, searches t

D-HAN The source code of D-HAN This is the source code of D-HAN: Dynamic News Recommendation with Hierarchical Attention Network. However, only the co

Collaborative Filtering - Netflix movie reviews Description This project consists of a collaborative filtering algorithm to predict movie reviews rati

Implementation-of-a-hadoop-based-movie-recommendation-system 通过编写代码，设计一个基于Hadoop的电影推荐系统，通过此推荐系统的编写，掌握在Hadoop平台上的文件操作，数据处理的技能。windows 10 hadoop 2.8.3 p

concierge A continuous learning collaborative filter1 deployed with a light web server2. Distributed updates are live (real time pubsub + delta traini

Introduction QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and

Movie-Success-Prediction A simple python program which predicts the success of a movie based on it's type, actor, actress and director. The program us

Movie Pundit Find your next flick by asking the (almost) all-knowing Movie Pundit Jump to Project Source » View Demo · Report Bug · Request Feature Ta

BiCAT This is our TensorFlow implementation for the paper: "BiCAT: Sequential Recommendation with Bidirectional Chronological Augmentation of Transfor

MovieTrends Project to analyse the daily movie trends from the website The Movie DataBase. The main idea is upload the results to a PostgreSQL server

fulltmdb A wrapper for The Movie Database API v3 and v4 that only uses the read access token (not api key). Installation Use the package manager pip t

Machine Learning for Argument-Based Computational Persuasion This repo contains the source code and a benchmark for predicting user's utilities with M

Sequential Recommendation Datasets This repository collects some commonly used sequential recommendation datasets in recent research papers and provid

Tiny-NewsRec The source codes for our paper "Tiny-NewsRec: Efficient and Effective PLM-based News Recommendation". Requirements PyTorch == 1.6.0 Tensor

DenoisingRec Adaptive Denoising Training for Recommendation. This is the pytorch implementation of our paper at WSDM 2021: Denoising Implicit Feedback

TorchRec (Experimental Release) TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale

torcp A script copies movie and TV files to your GD drive, or create Hard Link in a seperate dir, in Emby-happy struct. Usage: python3 torcp.py -h Exa

ff3 - Format Preserving Encryption in Python An implementation of the NIST approved FF3 and FF3-1 Format Preserving Encryption (FPE) algorithms in Pyt

Food Recommendations Desktop application created with python and tkinter. The goal for this application is to provide a way for users to enter and sav

NeuroMLR: Robust & Reliable Route Recommendation on Road Networks This repository is the official implementation of NeuroMLR: Robust & Reliable Route

Curriculum_disentangled_recommendation This is the repository of the NeurIPS 2021 paper "Curriculum Disentangled Recommendation with Noisy Multi-feedb

FOREC: A Cross-Market Recommendation System This repository provides the implementation of our CIKM 2021 paper titled as "Cross-Market Product Recomme

🔍 E-Commerce recommender demo 🔍 This is a simple stream setup that uses Memgraph to ingest real-time data from a simulated online store. Data is str

PERSIA (Parallel rEcommendation tRaining System with hybrId Acceleration) is developed by AI platform@Kuaishou Technology, collaborating with ETH. It

README 0. 초록 1) 목적 사용자의 Needs를 기반으로 영화를 추천해주는 커뮤니티 서비스 구현 2) p!ck 서비스란? "pick your taste!" 취향대로 영화 플레이리스트(이하 서비스 내에서의 명칭인 '바스켓'이라 함)를 만들고, 비슷한 취향을 가진

TiSASRec.paddle A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation. Introduction 论文：Time Interval Aware Sel

SURGE: Sequential Recommendation with Graph Neural Networks This is our TensorFlow implementation for the paper: Sequential Recommendation with Graph

⚡ funk-svd funk-svd is a Python 3 library implementing a fast version of the famous SVD algorithm popularized by Simon Funk during the Neflix Prize co

RecGURU About The Project Source code and baselines for the RecGURU paper "RecGURU: Adversarial Learning of Generalized User Representations for Cross

GRecX A Fair Benchmark for GNN-based Recommendation Homepage and Documentation Homepage: Documentation: Paper: GRecX: An Efficient and Unified Benchma

music-recommender-rest-api A music recommendation REST API which makes a machine learning algorithm work with the Django REST Framework How it works T

Diabetes-detection-tg-bot A Recommendation System For Diabetes Detection And Treatment Данная система помогает определить наличие или отсутствие сахар

Easy to start. Use deep nerual network to predict the sentiment of movie review. Various methods, word2vec, tf-idf and df to generate text vectors. Various models including lstm and cov1d. Achieve f1 score 92.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

High performance distributed framework for training deep learning recommendation models based on PyTorch.

Session-aware Item-combination Recommendation with Transformer Network 2nd place (0.39224) code and report for IEEE BigData Cup 2021 Track1 Report EDA

TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations Requirements python 3.6 torch 1.9 numpy 1.19 Quick Start The experimen

recommendersystem Recommendation System to recommend top books from the dataset Introduction The recom.py is the main program code. The dataset is als

HandGestureDetection This program uses a Haar Cascade algorithm to detect the presence of your hand, and then passes it on to a self-created and self-

Fair Recommendation in Two-Sided Platforms

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks This is our Pytorch implementation for the paper: Zirui Zhu, Chen Gao, Xu C

Contrastive Learning for Cold-start Recommendation This is our Pytorch implementation for the paper: Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan L

IMDb Discord Bot how to run this bot. the first step you must create prefixes.json file the second step you must create a virtualenv if you use window