ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms

Overview

ToR[e]cSys


News

It is happy to know the new package of Tensorflow Recommenders.


ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop a ecosystem to experiment, share, reproduce, and deploy in real world in a smooth and easy way (Hope it can be done).

Installation

TBU

Documentation

The complete documentation for ToR[e]cSys is available via ReadTheDocs website.
Thank you for ReadTheDocs! You are the best!

Implemented Models

1. Subsampling

Model Name Research Paper Year
Word2Vec Omer Levy et al, 2015. Improving Distributional Similarity with Lessons Learned from Word Embeddings 2015

2. Negative Sampling

Model Name Research Paper Year
TBU

3. Click through Rate (CTR) Model

Model Name Research Paper Year
Logistic Regression / /
Factorization Machine Steffen Rendle, 2010. Factorization Machine 2010
Factorization Machine Support Neural Network Weinan Zhang et al, 2016. Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction 2016
Field-Aware Factorization Machine Yuchin Juan et al, 2016. Field-aware Factorization Machines for CTR Prediction 2016
Product Neural Network Yanru QU et al, 2016. Product-based Neural Networks for User Response Prediction 2016
Attentional Factorization Machine Jun Xiao et al, 2017. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks 2017
Deep and Cross Network Ruoxi Wang et al, 2017. Deep & Cross Network for Ad Click Predictions 2017
Deep Factorization Machine Huifeng Guo et al, 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction 2017
Neural Collaborative Filtering Xiangnan He et al, 2017. Neural Collaborative Filtering 2017
Neural Factorization Machine Xiangnan He et al, 2017. Neural Factorization Machines for Sparse Predictive Analytics 2017
eXtreme Deep Factorization Machine Jianxun Lian et al, 2018. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems 2018
Deep Field-Aware Factorization Machine Junlin Zhang et al, 2019. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine 2019
Deep Matching Correlation Prediction Wentao Ouyang et al, 2019. Representation Learning-Assisted Click-Through Rate Prediction 2019
Deep Session Interest Network Yufei Feng et al, 2019. Deep Session Interest Network for Click-Through Rate Prediction 2019
Elaborated Entire Space Supervised Multi Task Model Hong Wen et al, 2019. Conversion Rate Prediction via Post-Click Behaviour Modeling 2019
Entire Space Multi Task Model Xiao Ma et al, 2019. Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate 2019
Field Attentive Deep Field Aware Factorization Machine Junlin Zhang et al, 2019. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine 2019
Position-bias aware learning framework Huifeng Guo et al, 2019. PAL: a position-bias aware learning framework for CTR prediction in live recommender systems 2019

4. Embedding Model

Model Name Research Paper Year
Matrix Factorization / /
Starspace Ledell Wu et al, 2017 StarSpace: Embed All The Things! 2017

5. Learning-to-Rank (LTR) Model

Model Name Research Paper Year
Personalized Re-ranking Model Changhua Pei et al, 2019. Personalized Re-ranking for Recommendation 2019

Getting Started

There are several ways using ToR[e]cSys to develop a Recommendation System. Before talking about them, we first need to discuss about components of ToR[e]cSys.

A model in ToR[e]cSys is constructed by two parts mainly: inputs and model, and they will be wrapped into a sequential module (torecsys.models.sequential) to be trained by Trainer (torecsys.trainer.Trainer). \

For inputs module (torecsys.inputs), it will handle most kinds of inputs in recommendation system, like categorical features, images, etc, with several kinds of methods, including token embedding, pre-trained image models, etc.

For models module (torecsys.models), it will implement some famous models in recommendation system, like Factorization Machine family. I hope I can make the library rich. To construct a model in the module, in addition to the modules implemented in PyTorch, I will also implement some layers in torecsys.layers which are called by models usually.

After the explanation of ToR[e]cSys, let's move on to the Getting Started. We can use ToR[e]cSys in the following ways:

  1. Run by command-line (In development)

    
    

torecsys build --inputs_config='{}'
--model_config='{"method":"FM", "embed_size": 8, "num_fields": 2}'
--regularizer_config='{"weight_decay": 0.1}'
--criterion_config='{"method": "MSELoss"}'
--optimizer_config='{"method": "SGD", "lr": "0.01"}'
... ```

  1. Run by class method

    
    

import torecsys as trs

build trainer by class method

trainer = trs.trainer.Trainer()
.bind_objective("CTR")
.set_inputs()
.set_model(method="FM", embed_size=8, num_fields=2)
.set_sequential()
.set_regularizer(weight_decay=0.1)
.build_criterion(method="MSELoss")
.build_optimizer(method="SGD", lr="0.01")
.build_loader(name="train", ...)
.build_loader(name="eval", ...)
.set_target_fields("labels")
.set_max_num_epochs(10)
.use_cuda()

start to fit the model

trainer.fit() ```

  1. Run like PyTorch Module

    
    

import torch import torch.nn as nn import torecsys as trs

some codes here

inputs = trs.inputs.InputsWrapper(schema=schema) model = trs.models.FactorizationMachineModel(embed_size=8, num_fields=2)

for i in range(epochs): optimizer.zero_grad() outputs = model(**inputs(batches)) loss = criterion(outputs, labels) loss.backward() optimizer.step() ```

(In development) You can anyone you like to train a Recommender System and serve it in the following ways:

  1. Run by command-line

    > torecsys serve --load_from='{}'
  2. Run by class method

    
    

import torecsys as trs

serving = trs.serving.Model()
.load_from(filepath=filepath) .run() ```

  1. Serve it yourself

    
    

from flask import Flask, request import torecsys as trs

model = trs.serving.Model()
.load_from(filepath=filepath)

@app.route("/predict") def predict(): args = request.json inference = model.predict(args) return inference, 200

if name == "main": app.run() ```

For further details, please refer to the example in repository or read the documentation. Hope you enjoy~

Examples

TBU

Sample Codes

TBU

Sample of Experiments

TBU

Authors

License

ToR[e]cSys is MIT-style licensed, as found in the LICENSE file.

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
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

RecSim NG, a probabilistic platform for multi-agent recommender systems simulation. RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a power

Google Research 110 Dec 16, 2022
Incorporating User Micro-behaviors and Item Knowledge 59 60 3 into Multi-task Learning for Session-based Recommendation

MKM-SR Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation Paper data and code This is the

ciecus 38 Dec 05, 2022
A library of Recommender Systems

A library of Recommender Systems This repository provides a summary of our research on Recommender Systems. It includes our code base on different rec

MilaGraph 980 Jan 05, 2023
Price-aware Recommendation with Graph Convolutional Networks,

PUP This is the official implementation of our ICDE'20 paper: Yu Zheng, Chen Gao, Xiangnan He, Yong Li, Depeng Jin, Price-aware Recommendation with Gr

S4rawBer2y 3 Oct 30, 2022
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

DANSER-WWW-19 This repository holds the codes for Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recom

Qitian Wu 78 Dec 10, 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
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
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
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
RecList is an open source library providing behavioral, "black-box" testing for recommender systems.

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

Jacopo Tagliabue 375 Dec 30, 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
Group-Buying Recommendation for Social E-Commerce

Group-Buying Recommendation for Social E-Commerce This is the official implementation of the paper Group-Buying Recommendation for Social E-Commerce (

Jun Zhang 37 Nov 28, 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
Recommender System Papers

Included Conferences: SIGIR 2020, SIGKDD 2020, RecSys 2020, CIKM 2020, AAAI 2021, WSDM 2021, WWW 2021

RUCAIBox 704 Jan 06, 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
Movie Recommender System

Movie-Recommender-System Movie-Recommender-System is a web application using which a user can select his/her watched movie from list and system will r

1 Jul 14, 2022
reXmeX is recommender system evaluation metric library.

A general purpose recommender metrics library for fair evaluation.

AstraZeneca 258 Dec 22, 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

3.6k Dec 30, 2022
A Python scikit for building and analyzing recommender systems

Overview Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. Surprise was designed with th

Nicolas Hug 5.7k Jan 01, 2023