ToR[e]cSys

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News

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

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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 an ecosystem to experiment, share, reproduce, and deploy in real world in a smooth and easy way (Hope it can be done).

• Installation
• Implemented Models
• Documentation
• Getting Started
• Examples
• Authors
• License

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 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](https://github.com/p768lw