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

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Recommender SystemsMachine-Learning-Recommendation-System
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?

  1. Benefits users in finding items of their interest.
  2. Help item providers in delivering their items to the right user.
  3. Identity products that are most relevant to users.
  4. Personalized content.
  5. 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.

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Happy N. Monday
Wavelet Transform | Machine Learning | Computer Vision | Deep Learning | Image Processing | AI
Happy N. Monday
GitHub Repository
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.

COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype

Xin Xia 43 Jan 04, 2023
6002project-rl - An implemention of offline RL on recommender system

An implemention of offline RL on recommender system @author: misajie @update: 20

Tzay Lee 3 May 24, 2022
A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021)

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

31 Jan 04, 2023
Attentive Social Recommendation: Towards User And Item Diversities

ASR This is a Tensorflow implementation of the paper: Attentive Social Recommendation: Towards User And Item Diversities Preprint, https://arxiv.org/a

Dongsheng Luo 1 Nov 14, 2021
Recommendation Systems for IBM Watson Studio platform

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

Milad Sadat-Mohammadi 1 Jan 21, 2022
[ICDMW 2020] Code and dataset for "DGTN: Dual-channel Graph Transition Network for Session-based Recommendation"

DGTN: Dual-channel Graph Transition Network for Session-based Recommendation This repository contains PyTorch Implementation of ICDMW 2020 (NeuRec @ I

Yujia 25 Nov 17, 2022
This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)

GHCF This is our implementation of the paper: Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu and Shaoping Ma. 2

Chong Chen 53 Dec 05, 2022
Elliot is a comprehensive recommendation framework that analyzes the recommendation problem from the researcher's perspective.

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Information Systems Lab @ Polytechnic University of Bari 215 Nov 29, 2022
QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

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 newly state-of-the-art recommendation models are implemented.

Yu 1.4k Dec 27, 2022
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation

RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation Pytorch based implemention of Relational Temporal

28 Dec 28, 2022
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

Introduction This is the repository of our accepted CIKM 2021 paper "Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Trans

SeqRec 29 Dec 09, 2022
Learning Fair Representations for Recommendation: A Graph-based Perspective, WWW2021

FairGo WWW2021 Learning Fair Representations for Recommendation: A Graph-based Perspective As a key application of artificial intelligence, recommende

lei 39 Oct 26, 2022
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Annoy Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given quer

Spotify 10.6k Jan 01, 2023
Spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

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

Jose A Dianes 794 Dec 23, 2022
Accuracy-Diversity Trade-off in Recommender Systems via Graph Convolutions

Accuracy-Diversity Trade-off in Recommender Systems via Graph Convolutions This repository contains the code of the paper "Accuracy-Diversity Trade-of

2 Sep 16, 2022
Collaborative variational bandwidth auto-encoder (VBAE) for recommender systems.

Collaborative Variational Bandwidth Auto-encoder The codes are associated with the following paper: Collaborative Variational Bandwidth Auto-encoder f

Yaochen Zhu 14 Dec 11, 2022
Deep recommender models using PyTorch.

Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various poin

Maciej Kula 2.8k Dec 29, 2022
An open source movie recommendation WebApp build by movie buffs and mathematicians that uses cosine similarity on the backend.

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

Kapil Pramod Deshmukh 8 May 28, 2022
E-Commerce recommender demo with real-time data and a graph database

🔍 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

g-despot 3 Feb 23, 2022
大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、DeepWalk、SSR、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、ListWise等

(中文文档|简体中文|English) 什么是推荐系统? 推荐系统是在互联网信息爆炸式增长的时代背景下，帮助用户高效获得感兴趣信息的关键； 推荐系统也是帮助产品最大限度吸引用户、留存用户、增加用户粘性、提高用户转化率的银弹。 有无数优秀的产品依靠用户可感知的推荐系统建立了良好的口碑，也有无数的公司依

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