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

Vector AI — A platform for building vector based applications. Encode, query and analyse data using vectors.

Source code of generalized shuffled linear regression

This repository contains the code for the paper ``Identifiable VAEs via Sparse Decoding''.

Official code for our EMNLP2021 Outstanding Paper MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks

Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

Facestar dataset. High quality audio-visual recordings of human conversational speech.

a morph transfer UGATIT for image translation.

Contrastive Feature Loss for Image Prediction

EfficientMPC - Efficient Model Predictive Control Implementation

Code for Graph-to-Tree Learning for Solving Math Word Problems (ACL 2020)

FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

Train a deep learning net with OpenStreetMap features and satellite imagery.

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

Auditing Black-Box Prediction Models for Data Minimization Compliance

A simple library that implements CLIP guided loss in PyTorch.

Source Code for DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances (https://arxiv.org/pdf/2012.01775.pdf)

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