A movie recommender which recommends the movies belonging to the genre that user has liked the most.

Last update: Mar 31, 2022

Overview

Content-Based-Movie-Recommender-System

This model relies on the similarity of the items being recommended.

(I have used Pandas and Numpy. However others may also use different libraries)

A content based recommender works with data that the user provides, either explicitly movie ratings for the MovieLens dataset. Based on that data, a user profile vector is generated, which is then used to make suggestions to the user.

As the user provides more inputs or takes actions on the recommendations, the weighted average vector of user becomes more accurate thus giving more accurate recommendations when data feeded is more.

Advantages :

Learns user's preferences

Highly personalized for the user

Disadvantages :

Low rated movie recommendation might occur if user choices were poor

Only genres watched and rated by user will be recommended and new genres will be hidden

User profile vector is highly static as the movie watched by user is mostly based on user's mentality and not user's previous history

A movie recommender which recommends the movies belonging to the genre that user has liked the most.

Related tags

Overview

Content-Based-Movie-Recommender-System

This model relies on the similarity of the items being recommended.

(I have used Pandas and Numpy. However others may also use different libraries)

A content based recommender works with data that the user provides, either explicitly movie ratings for the MovieLens dataset. Based on that data, a user profile vector is generated, which is then used to make suggestions to the user.

As the user provides more inputs or takes actions on the recommendations, the weighted average vector of user becomes more accurate thus giving more accurate recommendations when data feeded is more.

Advantages :

Learns user's preferences

Highly personalized for the user

Disadvantages :

Low rated movie recommendation might occur if user choices were poor

Only genres watched and rated by user will be recommended and new genres will be hidden

User profile vector is highly static as the movie watched by user is mostly based on user's mentality and not user's previous history

Owner

Srinivasan K

An open source movie recommendation WebApp build by movie buffs and mathematicians that uses cosine similarity on the backend.

RecList is an open source library providing behavioral, "black-box" testing for recommender systems.

Elliot is a comprehensive recommendation framework that analyzes the recommendation problem from the researcher's perspective.

A movie recommender which recommends the movies belonging to the genre that user has liked the most.

Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.

Pytorch domain library for recommendation systems

6002project-rl - An implemention of offline RL on recommender system

reXmeX is recommender system evaluation metric library.

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

Learning Fair Representations for Recommendation: A Graph-based Perspective, WWW2021

EXEMPLO DE SISTEMA ESPECIALISTA PARA RECOMENDAR SERIADOS EM PYTHON

Temporal Meta-path Guided Explainable Recommendation (WSDM2021)

The implementation of the submitted paper "Deep Multi-Behaviors Graph Network for Voucher Redemption Rate Prediction" in SIGKDD 2021 Applied Data Science Track.

QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

A Library for Field-aware Factorization Machines

E-Commerce recommender demo with real-time data and a graph database

Mutual Fund Recommender System. Tailor for fund transactions.

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs.

A Python scikit for building and analyzing recommender systems