Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'

Last update: Nov 22, 2022

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Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'

Paper, saved teacher models and Android Studio Project are available at Dropbox: https://www.dropbox.com/sh/g4mgdaezr8uqgic/AABpUxvY3_407D-0UNuEzOAMa?dl=0

Environments: Python 3.7, Pytorch 1.6.0, Android Studio 11.0.11, Pytorch Mobile.

Codes are based on RecBole (https://github.com/RUCAIBox/RecBole) and Pytorch Mobile Demo (https://github.com/pytorch/android-demo-app)

Steps to run models on Android Studio:

(1) Run mobile.py to save pytorch model into Android Studio.

(2) Select virtual device in Android Studio. (Google Pixel 2 API 29)

Owner

Xin Xia

PhD Candidate in The University of Queensland

GitHub Repository

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Codes for SIGIR'22 Paper 'On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation'

Related tags

Overview

OD-Rec

Owner

Xin Xia

Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

Code for our paper "Interactive Analysis of CNN Robustness"

Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.

CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching（CVPR2021）

This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution

Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Code for Understanding Pooling in Graph Neural Networks

Code and hyperparameters for the paper "Generative Adversarial Networks"

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📚 Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks.

Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

Mail classification with tensorflow and MS Exchange Server (ham or spam).

PyTorch evaluation code for Delving Deep into the Generalization of Vision Transformers under Distribution Shifts.

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