Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)

Overview

Debiasing Item-to-Item Recommendations With Small Annotated Datasets

This is the code for our RecSys '20 paper. Other materials can be found here:

Setup

This assumes that you have a recent Anaconda distribution of Python 3 installed. To install the dependencies,

conda env create -f environment.yml

Then, activate your new environment

conda activate item2item

and get the datasets

python 0_get_datasets.py

Running the demo

To run the command-line demo that allows you to retrieve item-to-item recommendations interactively,

python 1_run_demo.py

Then, follow the prompts

Input (partial) movie title [empty to quit]: toy
option #
0              Toy Story (1995)
1            Toy Story 2 (1999)
2            Toy Story 3 (2010)
3                   Toys (1992)
4       Babes in Toyland (1961)
5           Toy Soldiers (1991)
6               Toy, The (1982)
7            Toy Story 4 (2019)
8       Babes in Toyland (1934)
9    Toy Story of Terror (2013)

Input option (0-10) [empty to exit]: 0
Recommendations for  Toy Story (1995)
             DebiasedModel                                                      ItemKNN
                     title     score                                              title     score
0       Toy Story 2 (1999) -1.319431                                 Toy Story 2 (1999)  0.632260
1       Toy Story 3 (2010) -1.382858         Willy Wonka & the Chocolate Factory (1971)  0.554588
2      Finding Nemo (2003) -1.532166                          Back to the Future (1985)  0.547485
3  Incredibles, The (2004) -1.544819                              Monsters, Inc. (2001)  0.542195
4    Monsters, Inc. (2001) -1.571283                              Lion King, The (1994)  0.541657
5             Shrek (2001) -1.627429                               Bug's Life, A (1998)  0.538624
6           Shrek 2 (2004) -1.628034               Independence Day (a.k.a. ID4) (1996)  0.535614
7     Bug's Life, A (1998) -1.665477          Star Wars: Episode IV - A New Hope (1977)  0.535263
8       Ratatouille (2007) -1.672807                                     Aladdin (1992)  0.534045
9                Up (2009) -1.722887  Star Wars: Episode VI - Return of the Jedi (1983)  0.532928

Running the baselines

First, fit the models and pick the best on the validation set:

python 2a_find_best.py

Then, get the test set performances:

python 2b_eval_on_test.py

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
Transfer-Learn is an open-source and well-documented library for Transfer Learning.

Transfer-Learn is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consist

THUML @ Tsinghua University 2.2k Jan 03, 2023
Matplotlib Image labeller for classifying images

mpl-image-labeller Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui! For more

Ian Hunt-Isaak 5 Sep 24, 2022
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
Official repository for "Intriguing Properties of Vision Transformers" (2021)

Intriguing Properties of Vision Transformers Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, & Ming-Hsuan Yang P

Muzammal Naseer 155 Dec 27, 2022
[ICCV 2021] Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

MAED: Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation Getting Started Our codes are implemented and tested with pyth

ZiNiU WaN 176 Dec 15, 2022
A simple rest api serving a deep learning model that classifies human gender based on their faces. (vgg16 transfare learning)

this is a simple rest api serving a deep learning model that classifies human gender based on their faces. (vgg16 transfare learning)

crispengari 5 Dec 09, 2021
FPSAutomaticAiming——基于YOLOV5的FPS类游戏自动瞄准AI

FPSAutomaticAiming——基于YOLOV5的FPS类游戏自动瞄准AI 声明: 本项目仅限于学习交流,不可用于非法用途,包括但不限于:用于游戏外挂等,使用本项目产生的任何后果与本人无关! 简介 本项目基于yolov5,实现了一款FPS类游戏(CF、CSGO等)的自瞄AI,本项目旨在使用现

Fabian 246 Dec 28, 2022
pytorch implementation of GPV-Pose

GPV-Pose Pytorch implementation of GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting. (link) UPDATE A new version

40 Dec 01, 2022
Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

GAN stability This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converg

Lars Mescheder 885 Jan 01, 2023
The official implementation of Variable-Length Piano Infilling (VLI).

Variable-Length-Piano-Infilling The official implementation of Variable-Length Piano Infilling (VLI). (paper: Variable-Length Music Score Infilling vi

29 Sep 01, 2022
MicRank is a Learning to Rank neural channel selection framework where a DNN is trained to rank microphone channels.

MicRank: Learning to Rank Microphones for Distant Speech Recognition Application Scenario Many applications nowadays envision the presence of multiple

Samuele Cornell 20 Nov 10, 2022
The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation

PointNav-VO The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation Project Page | Paper Table of Contents Setup

Xiaoming Zhao 41 Dec 15, 2022
🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

3D Object Reconstruction from a Single Depth View with Adversarial Learning Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni

Bo Yang 125 Nov 26, 2022
Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion Models

Label-Efficient Semantic Segmentation with Diffusion Models Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion

Yandex Research 355 Jan 06, 2023
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation

Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation By Qiang Zhou*, Zilong Huang*, Lichao Huang, Han Shen, Yon

Forest 117 Apr 01, 2022
Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training"

Saliency Guided Training Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training" by Aya Abdelsalam Ismail, Hector Cor

8 Sep 22, 2022
[ICCV'2021] "SSH: A Self-Supervised Framework for Image Harmonization", Yifan Jiang, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Kalyan Sunkavalli, Simon Chen, Sohrab Amirghodsi, Sarah Kong, Zhangyang Wang

SSH: A Self-Supervised Framework for Image Harmonization (ICCV 2021) code for SSH Representative Examples Main Pipeline RealHM DataSet Google Drive Pr

VITA 86 Dec 02, 2022
offical implement of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021

LifelongReID Offical implementation of our Lifelong Person Re-Identification via Adaptive Knowledge Accumulation in CVPR2021 by Nan Pu, Wei Chen, Yu L

PeterPu 76 Dec 08, 2022
Deep Face Recognition in PyTorch

Face Recognition in PyTorch By Alexey Gruzdev and Vladislav Sovrasov Introduction A repository for different experimental Face Recognition models such

Alexey Gruzdev 141 Sep 11, 2022
Pipeline for employing a Lightweight deep learning models for LOW-power systems

PL-LOW A high-performance deep learning model lightweight pipeline that gradually lightens deep neural networks in order to utilize high-performance d

POSTECH Data Intelligence Lab 9 Aug 13, 2022