Recommender systems are the systems that are designed to recommend things to the user based on many different factors

Last update: Feb 15, 2022

Overview

Machine-Learning-Recommendation-System

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.

Why the Recommendation system?

Benefits users in finding items of their interest.
Help item providers in delivering their items to the right user.
Identity products that are most relevant to users.
Personalized content.
Help websites to improve user engagement.

What can be Recommended?

There are many different things that can be recommended by the system like movies, books, news, articles, jobs, advertisements, etc. Netflix uses a recommender system to recommend movies & web-series to its users. Similarly, YouTube recommends different videos. There are many examples of recommender systems that are widely used today.

Recommender systems are the systems that are designed to recommend things to the user based on many different factors

Related tags

Overview

Machine-Learning-Recommendation-System

Why the Recommendation system?

What can be Recommended?

Owner

Happy N. Monday

This is our Tensorflow implementation for "Graph-based Embedding Smoothing for Sequential Recommendation" (GES) (TKDE, 2021).

Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

An Efficient and Effective Framework for Session-based Social Recommendation

Self-supervised Graph Learning for Recommendation

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

Code for KHGT model, AAAI2021

Temporal Meta-path Guided Explainable Recommendation (WSDM2021)

A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021)

RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation

Code for my ORSUM, ACM RecSys 2020, HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation

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

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

fastFM: A Library for Factorization Machines

Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Fast Python Collaborative Filtering for Implicit Feedback Datasets

Handling Information Loss of Graph Neural Networks for Session-based Recommendation

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

Graph Neural Networks for Recommender Systems

Mutual Fund Recommender System. Tailor for fund transactions.

A Library for Field-aware Factorization Machines