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

Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch

A library of metrics for evaluating recommender systems

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

Real time recommendation playground

Spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

Price-aware Recommendation with Graph Convolutional Networks,

Code for MB-GMN, SIGIR 2021

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

The source code for "Global Context Enhanced Graph Neural Network for Session-based Recommendation".

[ICDMW 2020] Code and dataset for "DGTN: Dual-channel Graph Transition Network for Session-based Recommendation"

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

Recommendation System to recommend top books from the dataset

reXmeX is recommender system evaluation metric library.

Codes for AAAI'21 paper 'Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation'

基于个性化推荐的音乐播放系统

EXEMPLO DE SISTEMA ESPECIALISTA PARA RECOMENDAR SERIADOS EM PYTHON

Pytorch domain library for recommendation systems

Incorporating User Micro-behaviors and Item Knowledge 59 60 3 into Multi-task Learning for Session-based Recommendation

Graph Neural Networks for Recommender Systems