A library of Recommender Systems

Overview

A library of Recommender Systems

This repository provides a summary of our research on Recommender Systems. It includes our code base on different recommendation topics, a comprehensive reading list and a set of bechmark data sets.

Code Base

Currently, we are interested in sequential recommendation, feature-based recommendation and social recommendation.

Sequential Recommedation

Since users' interests are naturally dynamic, modeling users' sequential behaviors can learn contextual representations of users' current interests and therefore provide more accurate recommendations. In this project, we include some state-of-the-art sequential recommenders that empoly advanced sequence modeling techniques, such as Markov Chains (MCs), Recurrent Neural Networks (RNNs), Temporal Convolutional Neural Networks (TCN) and Self-attentive Neural Networks (Transformer).

Feature-based Recommendation

A general method for recommendation is to predict the click probabilities given users' profiles and items' features, which is known as CTR prediction. For CTR prediction, a core task is to learn (high-order) feature interactions because feature combinations are usually powerful indicators for prediction. However, enumerating all the possible high-order features will exponentially increase the dimension of data, leading to a more serious problem of model overfitting. In this work, we propose to learn low-dimentional representations of combinatorial features with self-attention mechanism, by which feature interactions are automatically implemented. Quantitative results show that our model have good prediction performance as well as satisfactory efficiency.

Social recommendation

Online social communities are an essential part of today's online experience. What we do or what we choose may be explicitly or implicitly influenced by our friends. In this project, we study the social influences in session-based recommendations, which simultaneously model users' dynamic interests and context-dependent social influences. First, we model users' dynamic interests with recurrent neural networks. In order to model context-dependent social influences, we propose to employ attention-based graph convolutional neural networks to differentiate friends' dynamic infuences in different behavior sessions.

Reading List

We maintain a reading list of RecSys papers to keep track of up-to-date research.

Data List

We provide a summary of existing benchmark data sets for evaluating recommendation methods.

New Data

We contribute a new large-scale dataset, which is collected from a popular movie/music/book review website Douban (www.douban.com). The data set could be useful for researches on sequential recommendation, social recommendation and multi-domain recommendation. See details here.

Publications:

Owner
MilaGraph
Research group led by Prof. Jian Tang at Mila-Quebec AI Institute (https://mila.quebec/) focusing on graph representation learning and graph neural networks.
MilaGraph
[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
Cross-Domain Recommendation via Preference Propagation GraphNet.

PPGN Codes for CIKM 2019 paper Cross-Domain Recommendation via Preference Propagation GraphNet. Citation Please cite our paper if you find this code u

Information Retrieval Group, Wuhan University, China 20 Dec 15, 2022
Mutual Fund Recommender System. Tailor for fund transactions.

Explainable Mutual Fund Recommendation Data Please see 'DATA_DESCRIPTION.md' for mode detail. Recommender System Methods Baseline Collabarative Fiilte

JHJu 2 May 19, 2022
A library of metrics for evaluating recommender systems

recmetrics A python library of evalulation metrics and diagnostic tools for recommender systems. **This library is activly maintained. My goal is to c

Claire Longo 458 Jan 06, 2023
Handling Information Loss of Graph Neural Networks for Session-based Recommendation

LESSR A PyTorch implementation of LESSR (Lossless Edge-order preserving aggregation and Shortcut graph attention for Session-based Recommendation) fro

Tianwen CHEN 62 Dec 03, 2022
A recommendation system for suggesting new books given similar books.

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

Sam Partee 2 Jan 06, 2022
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
This library intends to be a reference for recommendation engines in Python

Crab - A Python Library for Recommendation Engines

Marcel Caraciolo 85 Oct 04, 2021
A Python implementation of LightFM, a hybrid recommendation algorithm.

LightFM Build status Linux OSX (OpenMP disabled) Windows (OpenMP disabled) LightFM is a Python implementation of a number of popular recommendation al

Lyst 4.2k Jan 02, 2023
Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021

Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN) This is our implementation for the paper: Su, Y., Zhang, R., Erfani, S., &

26 Nov 22, 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
A tensorflow implementation of the RecoGCN model in a CIKM'19 paper, titled with "Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation".

This repo contains a tensorflow implementation of RecoGCN and the experiment dataset Running the RecoGCN model python train.py Example training outp

xfl15 30 Nov 25, 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
A framework for large scale recommendation algorithms.

A framework for large scale recommendation algorithms.

Alibaba Group - PAI 880 Jan 03, 2023
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
EXEMPLO DE SISTEMA ESPECIALISTA PARA RECOMENDAR SERIADOS EM PYTHON

exemplo-de-sistema-especialista EXEMPLO DE SISTEMA ESPECIALISTA PARA RECOMENDAR SERIADOS EM PYTHON Resumo O objetivo de auxiliar o usuário na escolha

Josue Lopes 3 Aug 31, 2021
Code for my ORSUM, ACM RecSys 2020, HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation

HeroGRAPH Code for my ORSUM @ RecSys 2020, HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation Paper, workshop pro

Qiang Cui 9 Sep 14, 2022
Recommendation System to recommend top books from the dataset

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

Vishal karur 1 Nov 15, 2021
This is our Tensorflow implementation for "Graph-based Embedding Smoothing for Sequential Recommendation" (GES) (TKDE, 2021).

Graph-based Embedding Smoothing (GES) This is our Tensorflow implementation for the paper: Tianyu Zhu, Leilei Sun, and Guoqing Chen. "Graph-based Embe

Tianyu Zhu 15 Nov 29, 2022
Pytorch domain library for recommendation systems

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

Meta Research 1.3k Jan 05, 2023